library(swimplot) library(coxphf) library(grid) library(gtable) library(readr) library(mosaic) library(dplyr) library(survival) library(survminer) library(gridtext) library(ggplot2) library(scales) library(officer) library(ggthemes) library(tidyverse) library(gtsummary) library(flextable) library(parameters) library(car) library(grid) library(ComplexHeatmap) library(readxl) library(janitor) library(rms) library(pROC) library(DT)

#Demographics Table by Altair Arm

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")

circ_data_subset1 <- circ_data %>%
  select(
    Age.Group,
    Sex,
    PrimSitev2,
    StageA.alt,
    p_hadNeo,
    p_TxAdjAltair,
    ctDNA1mo,
    p_AltBaselineWin,
    BRAF.V600E,
    RAS,
    MSI) %>%
  mutate(
    Age.Group = factor(Age.Group, levels = c("1", "2"), labels = c("<70", ">70")),
    Sex = factor(Sex, levels = c("Male", "Female")),
    PrimSitev2 = factor(PrimSitev2, levels = c("Right-sided colon", "Left-sided colon", "Rectum")),
    StageA.alt = factor(StageA.alt, levels = c("I", "II", "III", "IV")),
    p_hadNeo = factor(p_hadNeo, levels=c("FALSE","TRUE"), labels = c("No", "Yes")),
    p_TxAdjAltair = factor(p_TxAdjAltair, levels=c("FALSE","TRUE"), labels = c("No", "Yes")),
    ctDNA1mo = factor(ctDNA1mo, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive")),
    p_AltBaselineWin = factor(p_AltBaselineWin, levels = c("MRD", "OnTreatment", "Surveillance")),
    BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF wt", "BRAF V600E")),
    RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS wt", "RAS mut")),
    MSI = factor(MSI, levels = c("MSS", "MSI-High")))

circ_data1 <- read.csv("Altair 20240729 Dataset.csv")

circ_data_subset2 <- circ_data1 %>%
  select(
    Age.Group,
    Sex,
    PrimSitev2,
    StageA.alt,
    p_hadNeo,
    p_TxAdjAltair,
    ctDNA1mo,
    p_AltBaselineWin,
    BRAF.V600E,
    RAS,
    MSI,
    altair.Arm) %>%
  mutate(
    Age.Group = factor(Age.Group, levels = c("1", "2"), labels = c("<70", ">70")),
    Sex = factor(Sex, levels = c("Male", "Female")),
    PrimSitev2 = factor(PrimSitev2, levels = c("Right-sided colon", "Left-sided colon", "Rectum")),
    StageA.alt = factor(StageA.alt, levels = c("I", "II", "III", "IV")),
    p_hadNeo = factor(p_hadNeo, levels=c("FALSE","TRUE"), labels = c("No", "Yes")),
    p_TxAdjAltair = factor(p_TxAdjAltair, levels=c("FALSE","TRUE"), labels = c("No", "Yes")),
    ctDNA1mo = factor(ctDNA1mo, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive")),
    p_AltBaselineWin = factor(p_AltBaselineWin, levels = c("MRD", "OnTreatment", "Surveillance")),
    BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF wt", "BRAF V600E")),
    RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS wt", "RAS mut")),
    MSI = factor(MSI, levels = c("MSS", "MSI-High")),
    altair.Arm = factor(altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI")))
Overall <- circ_data_subset1 %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
Overall
Characteristic N = 2431
Age.Group
    <70 155 (64%)
    >70 88 (36%)
Sex
    Male 142 (58%)
    Female 101 (42%)
PrimSitev2
    Right-sided colon 60 (26%)
    Left-sided colon 127 (55%)
    Rectum 46 (20%)
    Unknown 10
StageA.alt
    I 10 (4.1%)
    II 58 (24%)
    III 109 (45%)
    IV 66 (27%)
p_hadNeo 87 (36%)
p_TxAdjAltair 112 (46%)
ctDNA1mo
    Negative 113 (47%)
    Positive 130 (53%)
p_AltBaselineWin
    MRD 58 (24%)
    OnTreatment 33 (14%)
    Surveillance 152 (63%)
BRAF.V600E
    BRAF wt 234 (96%)
    BRAF V600E 9 (3.7%)
RAS
    RAS wt 148 (61%)
    RAS mut 95 (39%)
MSI
    MSS 238 (98%)
    MSI-High 5 (2.1%)
1 n (%) 

ByctDNA_MRD <- circ_data_subset2 %>%
  tbl_summary(
    by = altair.Arm, # add this line to subgroup by altair.Arm
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels()
ByctDNA_MRD
Characteristic Placebo
N = 1211		 FTD/TPI
N = 1221		 p-value2
Age.Group

0.8
    <70 78 (64%) 77 (63%)
    >70 43 (36%) 45 (37%)
Sex

>0.9
    Male 71 (59%) 71 (58%)
    Female 50 (41%) 51 (42%)
PrimSitev2

>0.9
    Right-sided colon 29 (25%) 31 (26%)
    Left-sided colon 64 (56%) 63 (53%)
    Rectum 22 (19%) 24 (20%)
    Unknown 6 4
StageA.alt

0.6
    I 3 (2.5%) 7 (5.7%)
    II 30 (25%) 28 (23%)
    III 56 (46%) 53 (43%)
    IV 32 (26%) 34 (28%)
p_hadNeo 41 (34%) 46 (38%) 0.5
p_TxAdjAltair 56 (46%) 56 (46%) >0.9
ctDNA1mo

>0.9
    Negative 56 (46%) 57 (47%)
    Positive 65 (54%) 65 (53%)
p_AltBaselineWin

0.5
    MRD 30 (25%) 28 (23%)
    OnTreatment 19 (16%) 14 (11%)
    Surveillance 72 (60%) 80 (66%)
BRAF.V600E

0.5
    BRAF wt 118 (98%) 116 (95%)
    BRAF V600E 3 (2.5%) 6 (4.9%)
RAS

0.2
    RAS wt 79 (65%) 69 (57%)
    RAS mut 42 (35%) 53 (43%)
MSI

0.7
    MSS 118 (98%) 120 (98%)
    MSI-High 3 (2.5%) 2 (1.6%)
1 n (%)
2 Pearson’s Chi-squared test; Fisher’s exact test 

merged_table <- tbl_merge(tbls=list(Overall, ByctDNA_MRD))
merged_table
Characteristic
Table 1
Table 2
N = 2431 Placebo
N = 1211		 FTD/TPI
N = 1221		 p-value2
Age.Group


0.8
    <70 155 (64%) 78 (64%) 77 (63%)
    >70 88 (36%) 43 (36%) 45 (37%)
Sex


>0.9
    Male 142 (58%) 71 (59%) 71 (58%)
    Female 101 (42%) 50 (41%) 51 (42%)
PrimSitev2


>0.9
    Right-sided colon 60 (26%) 29 (25%) 31 (26%)
    Left-sided colon 127 (55%) 64 (56%) 63 (53%)
    Rectum 46 (20%) 22 (19%) 24 (20%)
    Unknown 10 6 4
StageA.alt


0.6
    I 10 (4.1%) 3 (2.5%) 7 (5.7%)
    II 58 (24%) 30 (25%) 28 (23%)
    III 109 (45%) 56 (46%) 53 (43%)
    IV 66 (27%) 32 (26%) 34 (28%)
p_hadNeo 87 (36%) 41 (34%) 46 (38%) 0.5
p_TxAdjAltair 112 (46%) 56 (46%) 56 (46%) >0.9
ctDNA1mo


>0.9
    Negative 113 (47%) 56 (46%) 57 (47%)
    Positive 130 (53%) 65 (54%) 65 (53%)
p_AltBaselineWin


0.5
    MRD 58 (24%) 30 (25%) 28 (23%)
    OnTreatment 33 (14%) 19 (16%) 14 (11%)
    Surveillance 152 (63%) 72 (60%) 80 (66%)
BRAF.V600E


0.5
    BRAF wt 234 (96%) 118 (98%) 116 (95%)
    BRAF V600E 9 (3.7%) 3 (2.5%) 6 (4.9%)
RAS


0.2
    RAS wt 148 (61%) 79 (65%) 69 (57%)
    RAS mut 95 (39%) 42 (35%) 53 (43%)
MSI


0.7
    MSS 238 (98%) 118 (98%) 120 (98%)
    MSI-High 5 (2.1%) 3 (2.5%) 2 (1.6%)
1 n (%)
2 Pearson’s Chi-squared test; Fisher’s exact test 

fit1 <- as_flex_table(
  merged_table,
  include = everything(),
  return_calls = FALSE
)
fit1

Table 1

Table 2

Characteristic

N = 2431

Placebo
N = 1211

FTD/TPI
N = 1221

p-value2

Age.Group

0.8

<70

155 (64%)

78 (64%)

77 (63%)

>70

88 (36%)

43 (36%)

45 (37%)

Sex

>0.9

Male

142 (58%)

71 (59%)

71 (58%)

Female

101 (42%)

50 (41%)

51 (42%)

PrimSitev2

>0.9

Right-sided colon

60 (26%)

29 (25%)

31 (26%)

Left-sided colon

127 (55%)

64 (56%)

63 (53%)

Rectum

46 (20%)

22 (19%)

24 (20%)

Unknown

10

6

4

StageA.alt

0.6

I

10 (4.1%)

3 (2.5%)

7 (5.7%)

II

58 (24%)

30 (25%)

28 (23%)

III

109 (45%)

56 (46%)

53 (43%)

IV

66 (27%)

32 (26%)

34 (28%)

p_hadNeo

87 (36%)

41 (34%)

46 (38%)

0.5

p_TxAdjAltair

112 (46%)

56 (46%)

56 (46%)

>0.9

ctDNA1mo

>0.9

Negative

113 (47%)

56 (46%)

57 (47%)

Positive

130 (53%)

65 (54%)

65 (53%)

p_AltBaselineWin

0.5

MRD

58 (24%)

30 (25%)

28 (23%)

OnTreatment

33 (14%)

19 (16%)

14 (11%)

Surveillance

152 (63%)

72 (60%)

80 (66%)

BRAF.V600E

0.5

BRAF wt

234 (96%)

118 (98%)

116 (95%)

BRAF V600E

9 (3.7%)

3 (2.5%)

6 (4.9%)

RAS

0.2

RAS wt

148 (61%)

79 (65%)

69 (57%)

RAS mut

95 (39%)

42 (35%)

53 (43%)

MSI

0.7

MSS

238 (98%)

118 (98%)

120 (98%)

MSI-High

5 (2.1%)

3 (2.5%)

2 (1.6%)

1n (%)

2Pearson's Chi-squared test; Fisher's exact test

save_as_docx(fit1, path = "~/Downloads/merged_table.docx")

#Median enrollment MTM/mL in the complete cohort

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)

median_val <- median(circ_data$p_AltBaselineMTM, na.rm = TRUE)
q1_val <- quantile(circ_data$p_AltBaselineMTM, 0.25, na.rm = TRUE)
q3_val <- quantile(circ_data$p_AltBaselineMTM, 0.75, na.rm = TRUE)
range_val <- range(circ_data$p_AltBaselineMTM, na.rm = TRUE)
cat("Median:", format(median_val, digits = 4), "\n")
Median: 0.4 
cat("Q1 (25th percentile):", q1_val, "\n")
Q1 (25th percentile): 0.1096503 
cat("Q3 (75th percentile):", q3_val, "\n")
Q3 (75th percentile): 1.767375 
cat("Range:", range_val, "\n")
Range: 0.02627334 250.47

#Median enrollment MTM/mL by Stage

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)
circ_data$StageA.alt <- factor(circ_data$StageA.alt, levels = c("I", "II", "III", "IV"))

stage_summary <- circ_data %>%
  group_by(StageA.alt) %>%
  summarise(
    Median = median(p_AltBaselineMTM, na.rm = TRUE),
    Q1 = quantile(p_AltBaselineMTM, 0.25, na.rm = TRUE),
    Q3 = quantile(p_AltBaselineMTM, 0.75, na.rm = TRUE),
    Min = min(p_AltBaselineMTM, na.rm = TRUE),
    Max = max(p_AltBaselineMTM, na.rm = TRUE)
  ) %>%
  ungroup()
print(stage_summary)

#Median enrollment MTM/mL by enrollment window

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)
circ_data$p_AltBaselineWin <- factor(circ_data$p_AltBaselineWin, levels = c("MRD", "OnTreatment", "Surveillance"))

stage_summary <- circ_data %>%
  group_by(p_AltBaselineWin) %>%
  summarise(
    Median = median(p_AltBaselineMTM, na.rm = TRUE),
    Q1 = quantile(p_AltBaselineMTM, 0.25, na.rm = TRUE),
    Q3 = quantile(p_AltBaselineMTM, 0.75, na.rm = TRUE),
    Min = min(p_AltBaselineMTM, na.rm = TRUE),
    Max = max(p_AltBaselineMTM, na.rm = TRUE)
  ) %>%
  ungroup()
print(stage_summary)

#Median enrollment MTM/mL by enrollment window in each treatment arm

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)
circ_data$p_AltBaselineWin <- factor(circ_data$p_AltBaselineWin, levels = c("MRD", "OnTreatment", "Surveillance"))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels = c("Control", "Experimental"), labels = c("Placebo", "FTD/TPI"))

# 1. Summary statistics by p_AltBaselineWin
stage_summary <- circ_data %>%
  group_by(p_AltBaselineWin) %>%
  summarise(
    Median = median(p_AltBaselineMTM, na.rm = TRUE),
    Q1 = quantile(p_AltBaselineMTM, 0.25, na.rm = TRUE),
    Q3 = quantile(p_AltBaselineMTM, 0.75, na.rm = TRUE),
    Min = min(p_AltBaselineMTM, na.rm = TRUE),
    Max = max(p_AltBaselineMTM, na.rm = TRUE)
  ) %>%
  ungroup()
print("Summary by p_AltBaselineWin:")
[1] "Summary by p_AltBaselineWin:"
print(stage_summary)

# 2. Median by p_AltBaselineWin and altair.Arm
arm_stage_summary <- circ_data %>%
  group_by(p_AltBaselineWin, altair.Arm) %>%
  summarise(
    Median = median(p_AltBaselineMTM, na.rm = TRUE),
    n = n()
  ) %>%
  ungroup()
`summarise()` has grouped output by 'p_AltBaselineWin'. You can override using the `.groups` argument.
print("Median p_AltBaselineMTM by p_AltBaselineWin and altair.Arm:")
[1] "Median p_AltBaselineMTM by p_AltBaselineWin and altair.Arm:"
print(arm_stage_summary)

# 3. Wilcoxon test p-values for Control vs Experimental within each p_AltBaselineWin
p_values <- circ_data %>%
  filter(!is.na(p_AltBaselineMTM), !is.na(altair.Arm), !is.na(p_AltBaselineWin)) %>%
  group_by(p_AltBaselineWin) %>%
  summarise(
    p_value = tryCatch(
      wilcox.test(p_AltBaselineMTM ~ altair.Arm)$p.value,
      error = function(e) NA
    )
  )
print("Wilcoxon test p-values by p_AltBaselineWin:")
[1] "Wilcoxon test p-values by p_AltBaselineWin:"
print(p_values)

#Median enrollment MTM/mL in Stage IV vs Non-Stage IV

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[!(circ_data$StageA.alt %in% c("I", "II", "III")),]
circ_datadf <- as.data.frame(circ_data)

median_val <- median(circ_data$p_AltBaselineMTM, na.rm = TRUE)
q1_val <- quantile(circ_data$p_AltBaselineMTM, 0.25, na.rm = TRUE)
q3_val <- quantile(circ_data$p_AltBaselineMTM, 0.75, na.rm = TRUE)
range_val <- range(circ_data$p_AltBaselineMTM, na.rm = TRUE)
cat("Median:", format(median_val, digits = 4), "\n")
Median: 0.6802 
cat("Q1 (25th percentile):", q1_val, "\n")
Q1 (25th percentile): 0.1848869 
cat("Q3 (75th percentile):", q3_val, "\n")
Q3 (75th percentile): 2.59763 
cat("Range:", range_val, "\n")
Range: 0.03444433 40.42593 
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)
circ_data$Stage.Final <- factor(circ_data$Stage.Final, levels = c("I-III", "IV"))
median_MTM <- aggregate(p_AltBaselineMTM ~ Stage.Final, data = circ_data, FUN = median)
print(median_MTM)
circ_data$Stage.Final <- factor(circ_data$Stage.Final, levels = c("I-III", "IV"))
boxplot(p_AltBaselineMTM~Stage.Final, data=circ_data, main="MTM/mL at enrollment", xlab="Stage", ylab="MTM/mL", col="white",border="black")

m1<-wilcox.test(p_AltBaselineMTM ~ Stage.Final, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)

    Wilcoxon rank sum test with continuity correction

data:  p_AltBaselineMTM by Stage.Final
W = 4740, p-value = 0.02394
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -0.41562942 -0.01469699
sample estimates:
difference in location 
            -0.1351274

#Number of patients with enrolment MTM/mL > various thresholds

rm(list = ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data$p_AltBaselineMTM <- as.numeric(circ_data$p_AltBaselineMTM)

# Define your cutoffs
cutoffs <- c(0.01, 0.047, 0.1, 0.179, 0.2, 0.3, 0.5, 1, 5, 8.172, 10)
total_pts <- nrow(circ_data)
for (co in cutoffs) {
  pts_above <- sum(circ_data$p_AltBaselineMTM >= co, na.rm = TRUE)
  perc      <- (pts_above / total_pts) * 100
  cat("Cutoff:", co, 
      "- Patients ≥ cutoff:", pts_above, 
      "- Percentage:", round(perc, 2), "%\n")
}
Cutoff: 0.01 - Patients ≥ cutoff: 243 - Percentage: 100 %
Cutoff: 0.047 - Patients ≥ cutoff: 229 - Percentage: 94.24 %
Cutoff: 0.1 - Patients ≥ cutoff: 189 - Percentage: 77.78 %
Cutoff: 0.179 - Patients ≥ cutoff: 158 - Percentage: 65.02 %
Cutoff: 0.2 - Patients ≥ cutoff: 150 - Percentage: 61.73 %
Cutoff: 0.3 - Patients ≥ cutoff: 135 - Percentage: 55.56 %
Cutoff: 0.5 - Patients ≥ cutoff: 111 - Percentage: 45.68 %
Cutoff: 1 - Patients ≥ cutoff: 78 - Percentage: 32.1 %
Cutoff: 5 - Patients ≥ cutoff: 29 - Percentage: 11.93 %
Cutoff: 8.172 - Patients ≥ cutoff: 23 - Percentage: 9.47 %
Cutoff: 10 - Patients ≥ cutoff: 18 - Percentage: 7.41 %

#DFS1 by TAS vs Placebo - All stages & stratified for Stage & ctDNA 1mo post-surgery

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b) ~ 
    altair.Arm, data = circ_data)

                          n events median 0.95LCL 0.95UCL
altair.Arm=Control      121     99   5.55    4.17    7.33
altair.Arm=Experimental 122     99   9.30    7.92   10.84
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - All Patients", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     55      66    0.455  0.0453       0.3642        0.540
   12     28      22    0.268  0.0408       0.1916        0.350
   18     16       5    0.215  0.0391       0.1443        0.296
   24      6       4    0.145  0.0394       0.0785        0.231

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     85      36    0.705  0.0413        0.615        0.777
   12     35      46    0.318  0.0428        0.236        0.402
   18     19      12    0.208  0.0380        0.139        0.287
   24     11       3    0.169  0.0370        0.104        0.248
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
circ_data$Disease.Stage <- factor(circ_data$Disease.Stage, levels=c("Stage II or lower","StageIII", "M1"))
circ_data$ctDNA1mo <- factor(circ_data$ctDNA1mo, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
cox_fit_stratified <- coxph(surv_object ~ altair.Arm + strata(Disease.Stage) + strata(ctDNA1mo), data = circ_data)
summary(cox_fit_stratified)
Call:
coxph(formula = surv_object ~ altair.Arm + strata(Disease.Stage) + 
    strata(ctDNA1mo), data = circ_data)

  n= 243, number of events= 198 

                     coef exp(coef) se(coef)      z Pr(>|z|)
altair.ArmFTD/TPI -0.2337    0.7916   0.1448 -1.614    0.107

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI    0.7916      1.263     0.596     1.051

Concordance= 0.555  (se = 0.021 )
Likelihood ratio test= 2.6  on 1 df,   p=0.1
Wald test            = 2.6  on 1 df,   p=0.1
Score (logrank) test = 2.61  on 1 df,   p=0.1
# Extract values for HR, 95% CI, and p-value
cox_fit_summary_stratified <- summary(cox_fit_stratified)
HR_stratified <- cox_fit_summary_stratified$coefficients[2]
lower_CI_stratified <- cox_fit_summary_stratified$conf.int[3]
upper_CI_stratified <- cox_fit_summary_stratified$conf.int[4]
p_value_stratified <- cox_fit_summary_stratified$coefficients[5]
label_text_stratified <- paste0("HR = ", round(HR_stratified, 2), 
                                " (", round(lower_CI_stratified, 2), "-", 
                                round(upper_CI_stratified, 2), "); p = ", 
                                round(p_value_stratified, 3))
print(label_text_stratified)
[1] "HR = 0.79 (0.6-1.05); p = 0.107"

#DFS1 by TAS vs Placebo - Excluding QC patients

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$QC.Exclude=="FALSE",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b) ~ 
    altair.Arm, data = circ_data)

                          n events median 0.95LCL 0.95UCL
altair.Arm=Control      117     99   5.52    4.11    6.47
altair.Arm=Experimental 118     99   9.30    7.82   10.18
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - Excluding those with QC Revisions", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     51      66    0.436  0.0458       0.3450        0.523
   12     24      22    0.242  0.0402       0.1679        0.324
   18     13       5    0.186  0.0381       0.1186        0.266
   24      5       4    0.113  0.0373       0.0532        0.198

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     81      36    0.695  0.0424       0.6027        0.769
   12     31      46    0.294  0.0427       0.2131        0.379
   18     15      12    0.179  0.0367       0.1143        0.257
   24      7       3    0.135  0.0355       0.0746        0.212
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.Arm, data = circ_data)

  n= 235, number of events= 198 

                     coef exp(coef) se(coef)    z Pr(>|z|)  
altair.ArmFTD/TPI -0.2425    0.7847   0.1426 -1.7   0.0891 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI    0.7847      1.274    0.5933     1.038

Concordance= 0.554  (se = 0.02 )
Likelihood ratio test= 2.88  on 1 df,   p=0.09
Wald test            = 2.89  on 1 df,   p=0.09
Score (logrank) test = 2.9  on 1 df,   p=0.09
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.78 (0.59-1.04); p = 0.089"

#DFS1 by TAS vs Placebo - Excluding Mets patients

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$Mets.Exclude=="FALSE",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b) ~ 
    altair.Arm, data = circ_data)

                          n events median 0.95LCL 0.95UCL
altair.Arm=Control      119     97   5.55    4.17    7.49
altair.Arm=Experimental 112     90   9.43    9.04   10.84
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - Excluding those with Mets prior to enrolment", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     55      64    0.462  0.0457       0.3708        0.549
   12     28      22    0.272  0.0413       0.1950        0.355
   18     16       5    0.219  0.0397       0.1467        0.300
   24      6       4    0.147  0.0400       0.0798        0.235

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     82      29    0.741  0.0414        0.649        0.812
   12     33      46    0.321  0.0447        0.235        0.409
   18     19      10    0.223  0.0404        0.149        0.306
   24     11       3    0.181  0.0394        0.112        0.264
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.Arm, data = circ_data)

  n= 231, number of events= 187 

                     coef exp(coef) se(coef)      z Pr(>|z|)  
altair.ArmFTD/TPI -0.2773    0.7579   0.1468 -1.889   0.0589 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI    0.7579       1.32    0.5684      1.01

Concordance= 0.559  (se = 0.02 )
Likelihood ratio test= 3.57  on 1 df,   p=0.06
Wald test            = 3.57  on 1 df,   p=0.06
Score (logrank) test = 3.59  on 1 df,   p=0.06
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.76 (0.57-1.01); p = 0.059"

#DFS1 by TAS vs Placebo - Stage I-III

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[!(circ_data$StageA.alt %in% c("IV")),]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b) ~ 
    altair.Arm, data = circ_data)

                         n events median 0.95LCL 0.95UCL
altair.Arm=Control      89     67   6.05    4.63    9.23
altair.Arm=Experimental 88     68   9.27    7.62   10.97
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - Stage I-III", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     45      44    0.506  0.0530        0.398        0.604
   12     24      16    0.324  0.0499        0.229        0.422
   18     15       2    0.291  0.0500        0.197        0.391
   24      6       3    0.212  0.0537        0.118        0.324

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     62      26    0.705  0.0486        0.597        0.788
   12     26      32    0.336  0.0509        0.238        0.435
   18     17       6    0.258  0.0480        0.170        0.355
   24     10       2    0.221  0.0477        0.136        0.320
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.Arm, data = circ_data)

  n= 177, number of events= 135 

                     coef exp(coef) se(coef)      z Pr(>|z|)
altair.ArmFTD/TPI -0.1532    0.8580   0.1725 -0.888    0.375

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI     0.858      1.166    0.6118     1.203

Concordance= 0.537  (se = 0.023 )
Likelihood ratio test= 0.79  on 1 df,   p=0.4
Wald test            = 0.79  on 1 df,   p=0.4
Score (logrank) test = 0.79  on 1 df,   p=0.4
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.86 (0.61-1.2); p = 0.375"

#DFS1 by TAS vs Placebo - Stage I-II

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[!(circ_data$StageA.alt %in% c("III","IV")),]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b) ~ 
    altair.Arm, data = circ_data)

                         n events median 0.95LCL 0.95UCL
altair.Arm=Control      33     20   18.0    5.52      NA
altair.Arm=Experimental 35     23   10.8    9.23      NA
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - Stage I-II", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     20      13    0.606  0.0851        0.420        0.749
   12     15       3    0.515  0.0870        0.335        0.669
   18     10       1    0.472  0.0897        0.292        0.633
   24      5       1    0.405  0.0991        0.215        0.587

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     29       6    0.829  0.0637        0.658        0.919
   12     13      15    0.395  0.0834        0.234        0.551
   18      9       1    0.364  0.0823        0.209        0.521
   24      5       1    0.312  0.0855        0.158        0.480
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.Arm, data = circ_data)

  n= 68, number of events= 43 

                      coef exp(coef) se(coef)     z Pr(>|z|)
altair.ArmFTD/TPI -0.02455   0.97575  0.30719 -0.08    0.936

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI    0.9757      1.025    0.5344     1.782

Concordance= 0.52  (se = 0.043 )
Likelihood ratio test= 0.01  on 1 df,   p=0.9
Wald test            = 0.01  on 1 df,   p=0.9
Score (logrank) test = 0.01  on 1 df,   p=0.9
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.98 (0.53-1.78); p = 0.936"

#DFS1 by TAS vs Placebo - Stage III

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[!(circ_data$StageA.alt %in% c("I", "II", "IV")),]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b) ~ 
    altair.Arm, data = circ_data)

                         n events median 0.95LCL 0.95UCL
altair.Arm=Control      56     47   5.44    3.71    9.00
altair.Arm=Experimental 53     45   7.82    6.14    9.73
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - Stage III", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     25      31   0.4464  0.0664       0.3142        0.570
   12      9      13   0.2089  0.0553       0.1129        0.325
   18      5       1   0.1828  0.0542       0.0914        0.299
   24      1       2   0.0914  0.0531       0.0212        0.226

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     33      20    0.623  0.0666       0.4784        0.738
   12     13      17    0.299  0.0633       0.1826        0.425
   18      8       5    0.184  0.0561       0.0900        0.305
   24      5       1    0.158  0.0539       0.0705        0.277
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.Arm, data = circ_data)

  n= 109, number of events= 92 

                     coef exp(coef) se(coef)      z Pr(>|z|)
altair.ArmFTD/TPI -0.2388    0.7876   0.2098 -1.138    0.255

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI    0.7876       1.27    0.5221     1.188

Concordance= 0.545  (se = 0.029 )
Likelihood ratio test= 1.3  on 1 df,   p=0.3
Wald test            = 1.3  on 1 df,   p=0.3
Score (logrank) test = 1.3  on 1 df,   p=0.3
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.79 (0.52-1.19); p = 0.255"

#DFS1 by TAS vs Placebo - Stage IV

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[!(circ_data$StageA.alt %in% c("I", "II", "III")),]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b) ~ 
    altair.Arm, data = circ_data)

                         n events median 0.95LCL 0.95UCL
altair.Arm=Control      32     32   3.96    3.71    7.98
altair.Arm=Experimental 34     31   9.76    7.62   11.76
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - Stage IV", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     10      22   0.3125  0.0819      0.16376        0.473
   12      4       6   0.1250  0.0585      0.03950        0.262
   18      1       3   0.0312  0.0308      0.00237        0.137

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     23      10   0.7047  0.0785      0.52049        0.829
   12      9      14   0.2757  0.0780      0.13790        0.433
   18      2       6   0.0919  0.0505      0.02355        0.219
   24      1       1   0.0460  0.0412      0.00431        0.175
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.Arm, data = circ_data)

  n= 66, number of events= 63 

                     coef exp(coef) se(coef)      z Pr(>|z|)  
altair.ArmFTD/TPI -0.6474    0.5234   0.2580 -2.509   0.0121 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI    0.5234      1.911    0.3157    0.8679

Concordance= 0.596  (se = 0.034 )
Likelihood ratio test= 6.24  on 1 df,   p=0.01
Wald test            = 6.3  on 1 df,   p=0.01
Score (logrank) test = 6.5  on 1 df,   p=0.01
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.52 (0.32-0.87); p = 0.012"

#DFS1 by TAS vs Placebo - ctDNA positive post-surgery

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$ctDNA1mo == "POSITIVE",]
circ_data <- subset(circ_data, !is.na(p_MRD))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b) ~ 
    altair.Arm, data = circ_data)

                         n events median 0.95LCL 0.95UCL
altair.Arm=Control      65     58   4.17    3.71    6.47
altair.Arm=Experimental 65     55   7.92    7.16   10.84
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - ctDNA positive post-surgery", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     24      41   0.3692  0.0599       0.2540        0.485
   12     11      12   0.1813  0.0482       0.0985        0.284
   18      5       4   0.1099  0.0408       0.0464        0.204
   24      2       1   0.0824  0.0388       0.0270        0.178

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     40      24    0.630  0.0600       0.5002        0.734
   12     14      25    0.233  0.0534       0.1374        0.343
   18      8       4    0.166  0.0473       0.0863        0.269
   24      6       0    0.166  0.0473       0.0863        0.269
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.Arm, data = circ_data)

  n= 130, number of events= 113 

                     coef exp(coef) se(coef)      z Pr(>|z|)  
altair.ArmFTD/TPI -0.3323    0.7173   0.1891 -1.757   0.0789 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI    0.7173      1.394    0.4952     1.039

Concordance= 0.56  (se = 0.026 )
Likelihood ratio test= 3.08  on 1 df,   p=0.08
Wald test            = 3.09  on 1 df,   p=0.08
Score (logrank) test = 3.12  on 1 df,   p=0.08
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.72 (0.5-1.04); p = 0.079"
#Fisher test for DFS percentages at 6, 12, 18 and 24 months
dfs_times <- c(6, 12, 18, 24)
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  pos_count <- sum(circ_data$altair.Arm == "Placebo" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  neg_total <- sum(circ_data$altair.Arm == "FTD/TPI")
  pos_total <- sum(circ_data$altair.Arm == "Placebo")
  
  neg_surv <- neg_total - sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  pos_surv <- pos_total - sum(circ_data$altair.Arm == "Placebo" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)
 p-value at 6 months p-value at 12 months p-value at 18 months p-value at 24 months 
         0.004815238          0.522656471          0.466593017          0.320823902

#DFS1 by TAS vs Placebo - ctDNA negative post-surgery

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$ctDNA1mo == "NEGATIVE",]
circ_data <- subset(circ_data, !is.na(ctDNA1mo))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b) ~ 
    altair.Arm, data = circ_data)

                         n events median 0.95LCL 0.95UCL
altair.Arm=Control      56     41   6.85    5.55    18.9
altair.Arm=Experimental 57     44  10.18    9.23    15.0
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - ctDNA negative post-surgery", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     31      25    0.554  0.0664       0.4147        0.672
   12     17      10    0.372  0.0650       0.2472        0.497
   18     11       1    0.350  0.0648       0.2272        0.476
   24      4       3    0.218  0.0742       0.0943        0.374

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     45      12    0.789  0.0540       0.6592        0.875
   12     21      21    0.414  0.0661       0.2848        0.539
   18     11       8    0.255  0.0602       0.1471        0.378
   24      5       3    0.170  0.0567       0.0771        0.294
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.Arm, data = circ_data)

  n= 113, number of events= 85 

                      coef exp(coef) se(coef)      z Pr(>|z|)
altair.ArmFTD/TPI -0.09568   0.90875  0.21911 -0.437    0.662

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI    0.9088        1.1    0.5915     1.396

Concordance= 0.538  (se = 0.03 )
Likelihood ratio test= 0.19  on 1 df,   p=0.7
Wald test            = 0.19  on 1 df,   p=0.7
Score (logrank) test = 0.19  on 1 df,   p=0.7
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.91 (0.59-1.4); p = 0.662"

#DFS2 by TAS vs Placebo - All stages

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS2.months, event = circ_data$p_evtDFS2)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS2.months, event = circ_data$p_evtDFS2) ~ 
    altair.Arm, data = circ_data)

                          n events median 0.95LCL 0.95UCL
altair.Arm=Control      121    100   5.55    4.17    7.33
altair.Arm=Experimental 122     99   9.30    7.92   10.84
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS2),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS2.months, event = circ_data$p_evtDFS2)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS2 by Arm", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     55      66    0.455  0.0453       0.3642        0.540
   12     27      23    0.259  0.0404       0.1842        0.341
   18     15       5    0.206  0.0386       0.1366        0.286
   24      6       4    0.139  0.0383       0.0746        0.223

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     85      36    0.705  0.0413        0.615        0.777
   12     35      46    0.318  0.0428        0.236        0.402
   18     19      12    0.208  0.0380        0.139        0.287
   24     11       3    0.169  0.0370        0.104        0.248
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.Arm, data = circ_data)

  n= 243, number of events= 199 

                     coef exp(coef) se(coef)      z Pr(>|z|)  
altair.ArmFTD/TPI -0.2549    0.7750   0.1421 -1.793    0.073 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI     0.775       1.29    0.5866     1.024

Concordance= 0.552  (se = 0.019 )
Likelihood ratio test= 3.21  on 1 df,   p=0.07
Wald test            = 3.22  on 1 df,   p=0.07
Score (logrank) test = 3.23  on 1 df,   p=0.07
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.78 (0.59-1.02); p = 0.073"

#OS by TAS vs Placebo - All stages

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$p_evtOS)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$p_evtOS) ~ 
    altair.Arm, data = circ_data)

                          n events median 0.95LCL 0.95UCL
altair.Arm=Control      121     10     NA      NA      NA
altair.Arm=Experimental 122     14     NA      NA      NA
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtOS),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$p_evtOS)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="OS by Arm - All Patients", ylab= "Overall Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6    121       0    1.000  0.0000        1.000        1.000
   12    118       2    0.983  0.0117        0.935        0.996
   18    105       1    0.974  0.0146        0.923        0.992
   24     85       1    0.965  0.0172        0.909        0.987

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6    122       0    1.000  0.0000        1.000        1.000
   12    119       3    0.975  0.0140        0.926        0.992
   18    105       1    0.967  0.0163        0.914        0.987
   24     78       5    0.914  0.0277        0.840        0.955
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.Arm, data = circ_data)

  n= 243, number of events= 24 

                    coef exp(coef) se(coef)     z Pr(>|z|)
altair.ArmFTD/TPI 0.3631    1.4378   0.4142 0.877    0.381

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI     1.438     0.6955    0.6385     3.238

Concordance= 0.574  (se = 0.054 )
Likelihood ratio test= 0.78  on 1 df,   p=0.4
Wald test            = 0.77  on 1 df,   p=0.4
Score (logrank) test = 0.78  on 1 df,   p=0.4
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 1.44 (0.64-3.24); p = 0.381"

#DFS1 by ctDNA MRD enrollment timepoint TAS vs Placebo

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$p_AltBaselineWin=="MRD",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b) ~ 
    altair.Arm, data = circ_data)

                         n events median 0.95LCL 0.95UCL
altair.Arm=Control      30     26   4.17    3.71    7.98
altair.Arm=Experimental 28     22   7.77    5.39   11.30
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - ctDNA MRD Enrollment", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     10      20    0.333  0.0861       0.1753        0.500
   12      6       4    0.200  0.0730       0.0812        0.356
   18      3       2    0.125  0.0625       0.0357        0.273
   24      2       0    0.125  0.0625       0.0357        0.273

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     18      10    0.643  0.0906        0.438        0.789
   12      7      11    0.250  0.0818        0.111        0.418
   18      5       0    0.250  0.0818        0.111        0.418
   24      4       0    0.250  0.0818        0.111        0.418
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.Arm, data = circ_data)

  n= 58, number of events= 48 

                     coef exp(coef) se(coef)      z Pr(>|z|)
altair.ArmFTD/TPI -0.3714    0.6897   0.2921 -1.271    0.204

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI    0.6897       1.45    0.3891     1.223

Concordance= 0.555  (se = 0.04 )
Likelihood ratio test= 1.62  on 1 df,   p=0.2
Wald test            = 1.62  on 1 df,   p=0.2
Score (logrank) test = 1.63  on 1 df,   p=0.2
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.69 (0.39-1.22); p = 0.204"
#Fisher test for DFS percentages at 6, 12, 18 and 24 months
dfs_times <- c(6, 12, 18, 24)
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  pos_count <- sum(circ_data$altair.Arm == "Placebo" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  neg_total <- sum(circ_data$altair.Arm == "FTD/TPI")
  pos_total <- sum(circ_data$altair.Arm == "Placebo")
  
  neg_surv <- neg_total - sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  pos_surv <- pos_total - sum(circ_data$altair.Arm == "Placebo" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)
 p-value at 6 months p-value at 12 months p-value at 18 months p-value at 24 months 
          0.03454285           0.75694899           0.32484791           0.32484791

#DFS1 by ctDNA On-treatment timepoint TAS vs Placebo

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$p_AltBaselineWin=="OnTreatment",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b) ~ 
    altair.Arm, data = circ_data)

                         n events median 0.95LCL 0.95UCL
altair.Arm=Control      19     16   9.07    4.30    17.5
altair.Arm=Experimental 14     13   6.64    2.99      NA
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - ctDNA On-treatment Enrollment", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     12       7    0.632  0.1107       0.3790        0.804
   12      5       7    0.263  0.1010       0.0958        0.468
   18      2       2    0.132  0.0829       0.0240        0.332

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6      7       7   0.5000  0.1336      0.22859        0.722
   12      2       4   0.1905  0.1119      0.03612        0.437
   18      1       1   0.0952  0.0876      0.00635        0.335
   24      1       0   0.0952  0.0876      0.00635        0.335
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.Arm, data = circ_data)

  n= 33, number of events= 29 

                    coef exp(coef) se(coef)     z Pr(>|z|)
altair.ArmFTD/TPI 0.1399    1.1502   0.3846 0.364    0.716

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI      1.15     0.8694    0.5413     2.444

Concordance= 0.516  (se = 0.053 )
Likelihood ratio test= 0.13  on 1 df,   p=0.7
Wald test            = 0.13  on 1 df,   p=0.7
Score (logrank) test = 0.13  on 1 df,   p=0.7
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 1.15 (0.54-2.44); p = 0.716"
#Fisher test for DFS percentages at 6, 12, 18 and 24 months
dfs_times <- c(6, 12, 18, 24)
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  pos_count <- sum(circ_data$altair.Arm == "Placebo" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  neg_total <- sum(circ_data$altair.Arm == "FTD/TPI")
  pos_total <- sum(circ_data$altair.Arm == "Placebo")
  
  neg_surv <- neg_total - sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  pos_surv <- pos_total - sum(circ_data$altair.Arm == "Placebo" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)
 p-value at 6 months p-value at 12 months p-value at 18 months p-value at 24 months 
           0.4969359            1.0000000            1.0000000            1.0000000

#DFS1 by ctDNA Surveillance timepoint TAS vs Placebo

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$p_AltBaselineWin=="Surveillance",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b) ~ 
    altair.Arm, data = circ_data)

                         n events median 0.95LCL 0.95UCL
altair.Arm=Control      72     57   5.57    4.11    9.33
altair.Arm=Experimental 80     64   9.73    9.13   11.76
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - ctDNA Surveillance Enrollment", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     33      39    0.458  0.0587       0.3408        0.568
   12     17      11    0.296  0.0551       0.1933        0.406
   18     11       1    0.278  0.0545       0.1780        0.388
   24      4       4    0.156  0.0565       0.0657        0.281

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     60      19    0.762  0.0477       0.6525        0.841
   12     26      31    0.362  0.0547       0.2568        0.468
   18     13      11    0.208  0.0473       0.1243        0.307
   24      6       3    0.151  0.0443       0.0774        0.248
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.Arm, data = circ_data)

  n= 152, number of events= 121 

                     coef exp(coef) se(coef)      z Pr(>|z|)
altair.ArmFTD/TPI -0.2391    0.7873   0.1829 -1.307    0.191

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI    0.7873       1.27    0.5501     1.127

Concordance= 0.559  (se = 0.025 )
Likelihood ratio test= 1.7  on 1 df,   p=0.2
Wald test            = 1.71  on 1 df,   p=0.2
Score (logrank) test = 1.72  on 1 df,   p=0.2
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.79 (0.55-1.13); p = 0.191"
#Fisher test for DFS percentages at 6, 12, 18 and 24 months
dfs_times <- c(6, 12, 18, 24)
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  pos_count <- sum(circ_data$altair.Arm == "Placebo" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  neg_total <- sum(circ_data$altair.Arm == "FTD/TPI")
  pos_total <- sum(circ_data$altair.Arm == "Placebo")
  
  neg_surv <- neg_total - sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  pos_surv <- pos_total - sum(circ_data$altair.Arm == "Placebo" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)
 p-value at 6 months p-value at 12 months p-value at 18 months p-value at 24 months 
        0.0001278464         0.3959706440         0.4666619773         0.6942842858

#Barplot with enrollment timepoint at any time by Arm

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")

circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
circ_data$p_AltBaselineWin <- factor(circ_data$p_AltBaselineWin, levels = c("MRD", "OnTreatment", "Surveillance"), labels = c("MRD", "On Treatment", "Surveillance"))
contingency_table <- table(circ_data$altair.Arm, circ_data$p_AltBaselineWin)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)

    Pearson's Chi-squared test

data:  contingency_table
X-squared = 1.2435, df = 2, p-value = 0.537
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.5471
alternative hypothesis: two.sided
print(contingency_table)
         
          MRD On Treatment Surveillance
  Placebo  30           19           72
  FTD/TPI  28           14           80
p_values <- c(chi_square_test$p.value, fisher_exact_test$p.value)
p_adjusted <- p.adjust(p_values, method = "bonferroni")
names(p_adjusted) <- c("Chi-Square Test", "Fisher's Exact Test")
print(p_adjusted)
    Chi-Square Test Fisher's Exact Test 
                  1                   1 
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "Enrollment timepoint", 
       x = "Arm", 
       y = "Patients (%)", 
       fill = "Enrollment timepoint",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Surveillance" = "lightblue", "On Treatment" = "lightgreen", "MRD" = "salmon")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size


#Calculate median MTM/mL for enrollment timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
result <- circ_data %>%
  group_by(p_AltBaselineWin) %>%
  summarise(
    Median = median(p_AltBaselineMTM, na.rm = TRUE),
    Range = paste(min(p_AltBaselineMTM, na.rm = TRUE), max(p_AltBaselineMTM, na.rm = TRUE), sep = " - ")
  )
print(result)

#DFS1 by TAS vs Placebo - All stages MTM/mL based on the lowest MTM/mL upon which the trial is positive

#Pts with MTM/mL≥0.047
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")

total_pts <- nrow(circ_data)
pts_MTM <- nrow(circ_data[circ_data$p_AltBaselineMTM >= 0.047,])
percentage_pts_MTM <- (pts_MTM / total_pts) * 100
print(paste0("Percentage of patients with MTM ≥ 0.047: ", round(percentage_pts_MTM, 2), "%"))
[1] "Percentage of patients with MTM ≥ 0.047: 94.24%"
circ_data <- circ_data[circ_data$p_AltBaselineMTM>=0.047,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b) ~ 
    altair.Arm, data = circ_data)

                          n events median 0.95LCL 0.95UCL
altair.Arm=Control      112     96   5.42    4.11    6.47
altair.Arm=Experimental 117     98   9.30    7.82   10.18
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - MTM/mL ≥0.047", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     48      64    0.429  0.0468       0.3360        0.518
   12     23      22    0.228  0.0401       0.1552        0.310
   18     12       5    0.173  0.0374       0.1074        0.252
   24      4       3    0.112  0.0375       0.0524        0.198

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     80      36    0.692  0.0427       0.5995        0.767
   12     31      45    0.296  0.0430       0.2150        0.382
   18     17      12    0.182  0.0370       0.1158        0.259
   24     10       3    0.143  0.0352       0.0824        0.219
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.Arm, data = circ_data)

  n= 229, number of events= 194 

                     coef exp(coef) se(coef)      z Pr(>|z|)  
altair.ArmFTD/TPI -0.2942    0.7451   0.1442 -2.041   0.0413 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI    0.7451      1.342    0.5617    0.9884

Concordance= 0.558  (se = 0.02 )
Likelihood ratio test= 4.15  on 1 df,   p=0.04
Wald test            = 4.16  on 1 df,   p=0.04
Score (logrank) test = 4.19  on 1 df,   p=0.04
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.75 (0.56-0.99); p = 0.041"
#Pts with MTM/mL<0.047
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")

total_pts <- nrow(circ_data)
pts_MTM <- nrow(circ_data[circ_data$p_AltBaselineMTM < 0.047,])
percentage_pts_MTM <- (pts_MTM / total_pts) * 100
print(paste0("Percentage of patients with MTM < 0.047: ", round(percentage_pts_MTM, 2), "%"))
[1] "Percentage of patients with MTM < 0.047: 5.76%"
circ_data <- circ_data[circ_data$p_AltBaselineMTM<0.047,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b) ~ 
    altair.Arm, data = circ_data)

                        n events median 0.95LCL 0.95UCL
altair.Arm=Control      9      3     NA    19.1      NA
altair.Arm=Experimental 5      1     NA      NA      NA
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - MTM/mL <0.047", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Control 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6      7       2    0.778   0.139        0.365        0.939
   12      5       0    0.778   0.139        0.365        0.939
   18      4       0    0.778   0.139        0.365        0.939
   24      2       1    0.583   0.198        0.157        0.855

                altair.Arm=Experimental 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6      5       0      1.0   0.000        1.000        1.000
   12      4       1      0.8   0.179        0.204        0.969
   18      2       0      0.8   0.179        0.204        0.969
   24      1       0      0.8   0.179        0.204        0.969
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.Arm, data = circ_data)

  n= 14, number of events= 4 

                     coef exp(coef) se(coef)      z Pr(>|z|)
altair.ArmFTD/TPI -0.5812    0.5592   1.1566 -0.502    0.615

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI    0.5592      1.788   0.05795     5.397

Concordance= 0.561  (se = 0.12 )
Likelihood ratio test= 0.28  on 1 df,   p=0.6
Wald test            = 0.25  on 1 df,   p=0.6
Score (logrank) test = 0.26  on 1 df,   p=0.6
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 0.56 (0.06-5.4); p = 0.615"
#Analysis for Likelihood-Ratio Interaction P value
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data$p_evtDFS1b  <- as.logical(circ_data$p_evtDFS1b)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)

circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
circ_data$ctDNA.MTM <- NA
circ_data <- circ_data %>%
  mutate(ctDNA.MTM = case_when(
    p_AltBaselineMTM<0.047 ~ 1,
    p_AltBaselineMTM>=0.047 ~ 2
  ))
circ_data$ctDNA.MTM <- factor(circ_data$ctDNA.MTM, levels=c("1","2"), labels = c("<0.047", "≥0.047"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
cox_model_main <- coxph(surv_object ~ ctDNA.MTM + altair.Arm, data = circ_data)
cox_model_interaction <- coxph(surv_object ~ ctDNA.MTM * altair.Arm, data = circ_data)
lrt_result <- anova(cox_model_main, cox_model_interaction, test = "LRT")
print(lrt_result)
Analysis of Deviance Table
 Cox model: response is  surv_object
 Model 1: ~ ctDNA.MTM + altair.Arm
 Model 2: ~ ctDNA.MTM * altair.Arm
   loglik  Chisq Df Pr(>|Chi|)
1 -937.59                     
2 -937.54 0.0991  1      0.753

#DFS1 by TAS vs Placebo - MTM/mL as continuous variable

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data$p_evtDFS1b  <- as.logical(circ_data$p_evtDFS1b)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$p_AltBaselineMTM <- as.numeric(circ_data$p_AltBaselineMTM)
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))

#############################################################################
# 3. Define cutoffs (0.01 to 100 on a normal numeric scale)
#############################################################################
cutoffs <- seq(0.01, 100, length.out = 50)

# Prepare a data frame to store results
results_df <- data.frame(
  cutoff = cutoffs,
  HR     = NA_real_,
  HR_low = NA_real_,
  HR_hi  = NA_real_,
  pval   = NA_real_,
  n_included = NA_integer_
)

#############################################################################
# 4. Loop over each cutoff: subselect data and fit Cox model (Placebo vs. TAS-102)
#############################################################################
for (i in seq_along(cutoffs)) {
  
  current_cutoff <- cutoffs[i]
  
  # Subset: patients with p_AltBaselineMTM >= current_cutoff
  sub_data <- circ_data %>%
    filter(p_AltBaselineMTM >= current_cutoff)
  
  # Re-factor in case any level is dropped
  sub_data$altair.Arm <- factor(sub_data$altair.Arm, levels = c("Placebo", "FTD/TPI"))
  
  # Only run the Cox model if both arms have at least some minimal data
  arm_counts <- table(sub_data$altair.Arm)
  if (length(arm_counts) == 2 && all(arm_counts >= 2)) {
    
    fit <- coxph(Surv(DFS.months, p_evtDFS1b) ~ altair.Arm, data = sub_data)
    fit_sum <- summary(fit)
    
    # Extract HR, 95% CI, and p-value
    hr          <- fit_sum$conf.int[,"exp(coef)"][1]
    hr_conf_low <- fit_sum$conf.int[,"lower .95"][1]
    hr_conf_hi  <- fit_sum$conf.int[,"upper .95"][1]
    pval        <- fit_sum$coefficients[,"Pr(>|z|)"][1]
    
    # Store in results_df
    results_df$HR[i]     <- hr
    results_df$HR_low[i] <- hr_conf_low
    results_df$HR_hi[i]  <- hr_conf_hi
    results_df$pval[i]   <- pval
    results_df$n_included[i] <- nrow(sub_data)
    
  } else {
    results_df$n_included[i] <- nrow(sub_data)
  }
}
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
G2;H2;Warningh in coxph.fit(X, Y, istrat, offset, init, control, weights = weights,  :
  Ran out of iterations and did not convergeg
#############################################################################
# 5. Plot: x-axis on log scale, y-axis with breaks at 0.05, 0.1, 0.25, 0.5, 1, 2
#############################################################################
plot_df <- results_df %>%
  filter(!is.na(HR))

# Plot
p <- ggplot(plot_df, aes(x = cutoff, y = HR)) +
  # Ribbon for confidence intervals
  geom_ribbon(aes(ymin = HR_low, ymax = HR_hi), alpha = 0.2) +
  # Line for the HR
  geom_line(size = 1) +
  # Reference line at HR=1
  geom_hline(yintercept = 1, linetype = "dashed", color = "red") +
  
  # X-axis on log scale
  scale_x_log10(
    breaks = c(0.01, 0.1, 1, 10, 100), 
    labels = c("0.01", "0.1", "1", "10", "100")
  ) +
  
  # Y-axis on normal (linear) scale with specific breaks
  scale_y_log10(
    breaks = c(0.1, 0.3, 1, 3),
    labels = c("0.1", "0.3", "1", "3"),
    limits = c(0.1, 3)   # Adjust or remove if needed
  ) +
  
  theme_bw(base_size = 14) +
  labs(
    title = "Hazard Ratio (Placebo vs. TAS-102) by MTM/mL",
    x     = "MTM/mL (log scale)",
    y     = "Hazard Ratio (log scale)"
  )

print(p)


# Find the 2nd MTM cutoff where the upper bound of the confidence interval (HR_hi) crosses HR = 1
crossing_point <- results_df %>% 
  filter(HR_hi >= 1) %>% 
  slice(2)  # Select the 2nd occurrence

# Print the cutoff value where HR_hi crosses 1
print(crossing_point$cutoff)
[1] 8.172449
#############################################################################
# 6. (Optional) Add vertical lines for specific cutoffs
#############################################################################
p + 
  geom_vline(
    xintercept = c(0.047, 0.179, 8.172),
    linetype   = "dashed",
    color      = "blue"
  ) +
  annotate(
    "text", 
    x     = c(0.179, 8.172), 
    y     = max(plot_df$HR_hi, na.rm = TRUE),
    label = c("0.179", "8.172"), 
    vjust = -0.5, 
    color = "blue"
  )

#Histogram for number of patients per enrolment MTM/mL

rm(list = ls())
setwd("~/Downloads")

df <- read.csv("Altair 20240729 Dataset.csv")
df$p_evtDFS1b      <- as.logical(df$p_evtDFS1b)
df$DFS.months      <- as.numeric(df$DFS.months)
df$p_AltBaselineMTM <- as.numeric(df$p_AltBaselineMTM)

# Clean the data
df <- df %>% filter(!is.na(p_AltBaselineMTM), p_AltBaselineMTM > 0)

# Descriptive stats (note: use the correct column name)
ppm_range  <- range(df$p_AltBaselineMTM)
lowest_ppm <- ppm_range[1]
highest_ppm <- ppm_range[2]
median_ppm <- median(df$p_AltBaselineMTM)

cat("Lowest PPM value :", lowest_ppm, "\n")
Lowest PPM value : 0.02627334 
cat("Highest PPM value:", highest_ppm, "\n")
Highest PPM value: 250.47 
cat("Median PPM value :", median_ppm, "\n")
Median PPM value : 0.4 
cat("Full range       :", lowest_ppm, "to", highest_ppm, "\n\n")
Full range       : 0.02627334 to 250.47 
# Histogram with custom log‑scale breaks
ggplot(df, aes(x = p_AltBaselineMTM)) +
  geom_histogram(bins = 100, fill = "gray80", color = "black") +
  scale_x_log10(
    breaks  = c(0.01, 0.1, 1, 10, 100),
    labels  = c("0.01", "0.1", "1", "10", "100")
  ) +
  labs(x = "p_AltBaselineMTM", y = "Number of samples") +
  ylim(0, 10) +
  theme_minimal()

#Enrollment MTM/mL by ctDNA clearance in TAS-102 vs Placebo Arms

#Placebo
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$altair.group.SAP_MSv2!="1b = Exclude: No on-Tx TPs",]
circ_data <- circ_data[circ_data$altair.Arm=="Control",]

# Transform p_MRD_MTM with log10
circ_data$p_AltBaselineMTM <- as.numeric(as.character(circ_data$p_AltBaselineMTM))
circ_data$p_evtDFS1b <- factor(circ_data$p_evtDFS1b, levels=c("TRUE","FALSE"), labels = c("Recurrence", "No Recurrence"))
circ_data$altair.resultPatW <- factor(circ_data$altair.resultPatW, levels=c("No clearance", "Transient clearance", "Sustained clearance"))
median_p_MRD_MTM <- aggregate(p_AltBaselineMTM ~ altair.resultPatW, data = circ_data, FUN = median)
print(median_p_MRD_MTM)

# Create violin plot with log10 scale on y-axis
ggplot(circ_data, aes(x=altair.resultPatW, y=p_AltBaselineMTM, fill=altair.resultPatW)) +
  geom_violin(trim=FALSE) +
  scale_fill_manual(values=c("Sustained clearance"="lightblue", "Transient clearance"="lightgreen", "No clearance"="salmon")) +
  geom_boxplot(width=0.1, fill="white", colour="black", alpha=0.5) +
  scale_y_log10(breaks=c(0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000)) +
  labs(title="Enrollment MTM/mL | Clearance - Placebo Arm", x="Clearance", y="Enrollment MTM/mL") +
  theme_minimal() +
  theme(legend.position="none")

m3_1v2 <- wilcox.test(p_AltBaselineMTM ~ altair.resultPatW,
                      data = circ_data[circ_data$altair.resultPatW %in% c("Sustained clearance", "Transient clearance"), ],
                      na.rm = TRUE)
print(m3_1v2)

    Wilcoxon rank sum exact test

data:  p_AltBaselineMTM by altair.resultPatW
W = 153, p-value = 0.09254
alternative hypothesis: true location shift is not equal to 0
m3_1v3 <- wilcox.test(p_AltBaselineMTM ~ altair.resultPatW,
                      data = circ_data[circ_data$altair.resultPatW %in% c("Sustained clearance", "No clearance"), ],
                      na.rm = TRUE)
print(m3_1v3)

    Wilcoxon rank sum test with continuity correction

data:  p_AltBaselineMTM by altair.resultPatW
W = 1053, p-value = 4.344e-06
alternative hypothesis: true location shift is not equal to 0
m3_2v3 <- wilcox.test(p_AltBaselineMTM ~ altair.resultPatW,
                      data = circ_data[circ_data$altair.resultPatW %in% c("Transient clearance", "No clearance"), ],
                      na.rm = TRUE)
print(m3_2v3)

    Wilcoxon rank sum test with continuity correction

data:  p_AltBaselineMTM by altair.resultPatW
W = 1010, p-value = 0.002179
alternative hypothesis: true location shift is not equal to 0
# Create a table with p-values
p_value_table <- data.frame(
  Comparison = c("Sustained vs Transient", "Sustained vs No Clearance", "Transient vs No Clearance"),
  P_Value = c(m3_1v2$p.value, m3_1v3$p.value, m3_2v3$p.value)
)
print(p_value_table)

#TAS-102
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$altair.group.SAP_MSv2!="1b = Exclude: No on-Tx TPs",]
circ_data <- circ_data[circ_data$altair.Arm=="Experimental",]

# Transform p_MRD_MTM with log10
circ_data$p_AltBaselineMTM <- as.numeric(as.character(circ_data$p_AltBaselineMTM))
circ_data$p_evtDFS1b <- factor(circ_data$p_evtDFS1b, levels=c("TRUE","FALSE"), labels = c("Recurrence", "No Recurrence"))
circ_data$altair.resultPatW <- factor(circ_data$altair.resultPatW, levels=c("No clearance", "Transient clearance", "Sustained clearance"))
median_p_MRD_MTM <- aggregate(p_AltBaselineMTM ~ altair.resultPatW, data = circ_data, FUN = median)
print(median_p_MRD_MTM)

# Create violin plot with log10 scale on y-axis
ggplot(circ_data, aes(x=altair.resultPatW, y=p_AltBaselineMTM, fill=altair.resultPatW)) +
  geom_violin(trim=FALSE) +
  scale_fill_manual(values=c("Sustained clearance"="lightblue", "Transient clearance"="lightgreen", "No clearance"="salmon")) +
  geom_boxplot(width=0.1, fill="white", colour="black", alpha=0.5) +
  scale_y_log10(breaks=c(0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000)) +
  labs(title="Enrollment MTM/mL | Clearance - TAS-102 Arm", x="Clearance", y="Enrollment MTM/mL") +
  theme_minimal() +
  theme(legend.position="none")

m3_1v2 <- wilcox.test(p_AltBaselineMTM ~ altair.resultPatW,
                      data = circ_data[circ_data$altair.resultPatW %in% c("Sustained clearance", "Transient clearance"), ],
                      na.rm = TRUE)
print(m3_1v2)

    Wilcoxon rank sum exact test

data:  p_AltBaselineMTM by altair.resultPatW
W = 241, p-value = 0.7395
alternative hypothesis: true location shift is not equal to 0
m3_1v3 <- wilcox.test(p_AltBaselineMTM ~ altair.resultPatW,
                      data = circ_data[circ_data$altair.resultPatW %in% c("Sustained clearance", "No clearance"), ],
                      na.rm = TRUE)
print(m3_1v3)

    Wilcoxon rank sum test with continuity correction

data:  p_AltBaselineMTM by altair.resultPatW
W = 449, p-value = 0.09739
alternative hypothesis: true location shift is not equal to 0
m3_2v3 <- wilcox.test(p_AltBaselineMTM ~ altair.resultPatW,
                      data = circ_data[circ_data$altair.resultPatW %in% c("Transient clearance", "No clearance"), ],
                      na.rm = TRUE)
print(m3_2v3)

    Wilcoxon rank sum test with continuity correction

data:  p_AltBaselineMTM by altair.resultPatW
W = 2201, p-value = 5.386e-06
alternative hypothesis: true location shift is not equal to 0
# Create a table with p-values
p_value_table <- data.frame(
  Comparison = c("Sustained vs Transient", "Sustained vs No Clearance", "Transient vs No Clearance"),
  P_Value = c(m3_1v2$p.value, m3_1v3$p.value, m3_2v3$p.value)
)
print(p_value_table)

#DFS1 by ctDNA Clearance with 3 groups - All stages

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$altair.group.SAP_MSv2!="1b = Exclude: No on-Tx TPs",]

circ_data$altair.resultPatW <- factor(circ_data$altair.resultPatW, levels=c("No clearance", "Transient clearance", "Sustained clearance"))
survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.resultPatW, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b) ~ 
    altair.resultPatW, data = circ_data)

                                        n events median 0.95LCL 0.95UCL
altair.resultPatW=No clearance        147    140   4.37    3.75    5.82
altair.resultPatW=Transient clearance  63     45  11.76   11.01   15.54
altair.resultPatW=Sustained clearance  25      5     NA   33.91      NA
event_summary <- circ_data %>%
  group_by(altair.resultPatW) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.resultPatW, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","green","blue"), title="DFS1 by Arm - ctDNA Clearance", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("No clearance", "Transient clearance",  "Sustained clearance"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.resultPatW, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.resultPatW=No clearance 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     60      87   0.4082  0.0405       0.3284       0.4862
   12     17      41   0.1262  0.0277       0.0782       0.1861
   18      9       6   0.0787  0.0232       0.0412       0.1319
   24      4       3   0.0472  0.0199       0.0183       0.0975

                altair.resultPatW=Transient clearance 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     57       5    0.920  0.0342       0.8192        0.966
   12     26      26    0.485  0.0653       0.3530        0.605
   18     10      10    0.290  0.0619       0.1767        0.414
   24      3       4    0.145  0.0600       0.0531        0.281

                altair.resultPatW=Sustained clearance 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     23       2    0.920  0.0543        0.716        0.979
   12     20       1    0.880  0.0650        0.673        0.960
   18     16       1    0.836  0.0752        0.619        0.935
   24     10       0    0.836  0.0752        0.619        0.935
circ_data$altair.resultPatW <- factor(circ_data$altair.resultPatW, levels=c("Sustained clearance", "Transient clearance", "No clearance"))
cox_fit <- coxph(surv_object ~ altair.resultPatW, data=circ_data) 
ggforest(cox_fit,data = circ_data)

summary(cox_fit)
Call:
coxph(formula = surv_object ~ altair.resultPatW, data = circ_data)

  n= 235, number of events= 190 

                                        coef exp(coef) se(coef)     z Pr(>|z|)    
altair.resultPatWTransient clearance  1.8292    6.2286   0.4812 3.801 0.000144 ***
altair.resultPatWNo clearance         2.8576   17.4200   0.4690 6.094  1.1e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                     exp(coef) exp(-coef) lower .95 upper .95
altair.resultPatWTransient clearance     6.229    0.16055     2.425     16.00
altair.resultPatWNo clearance           17.420    0.05741     6.948     43.67

Concordance= 0.686  (se = 0.017 )
Likelihood ratio test= 101.2  on 2 df,   p=<2e-16
Wald test            = 64.59  on 2 df,   p=9e-15
Score (logrank) test = 87.72  on 2 df,   p=<2e-16
cox_fit_summary <- summary(cox_fit)

#OS by ctDNA Clearance with 3 groups - All stages

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$altair.group.SAP_MSv2!="1b = Exclude: No on-Tx TPs",]

circ_data$altair.resultPatW <- factor(circ_data$altair.resultPatW, levels=c("No clearance", "Transient clearance", "Sustained clearance"))
survfit(Surv(time = circ_data$OS.months, event = circ_data$p_evtOS)~altair.resultPatW, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$p_evtOS) ~ 
    altair.resultPatW, data = circ_data)

                                        n events median 0.95LCL 0.95UCL
altair.resultPatW=No clearance        147     19     NA    40.7      NA
altair.resultPatW=Transient clearance  63      2     NA      NA      NA
altair.resultPatW=Sustained clearance  25      0     NA      NA      NA
event_summary <- circ_data %>%
  group_by(altair.resultPatW) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtOS),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$p_evtOS)
KM_curve <- survfit(surv_object ~ altair.resultPatW, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","green","blue"), title="OS by Arm - ctDNA Clearance", ylab= "Overall Survival", xlab="Time from Enrollment (Months)", legend.labs=c("No clearance", "Transient clearance",  "Sustained clearance"), legend.title="")

summary(KM_curve, times= c(6, 12, 18, 24))
Call: survfit(formula = surv_object ~ altair.resultPatW, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.resultPatW=No clearance 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6    147       0    1.000  0.0000        1.000        1.000
   12    143       3    0.979  0.0117        0.938        0.993
   18    124       2    0.965  0.0155        0.917        0.985
   24     96       5    0.923  0.0237        0.860        0.958

                altair.resultPatW=Transient clearance 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     63       0    1.000  0.0000        1.000        1.000
   12     63       0    1.000  0.0000        1.000        1.000
   18     57       0    1.000  0.0000           NA           NA
   24     43       1    0.978  0.0215        0.856        0.997

                altair.resultPatW=Sustained clearance 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     25       0        1       0            1            1
   12     25       0        1       0            1            1
   18     25       0        1       0            1            1
   24     21       0        1       0           NA           NA
circ_data$altair.resultPatW <- factor(circ_data$altair.resultPatW, levels=c("Sustained clearance", "Transient clearance", "No clearance"))
cox_fit <- coxphf(surv_object ~ altair.resultPatW, data=circ_data) 
summary(cox_fit)
coxphf(formula = surv_object ~ altair.resultPatW, data = circ_data)

Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood 

                                         coef se(coef) exp(coef) lower 0.95 upper 0.95     Chisq          p
altair.resultPatWTransient clearance 1.004483 1.609325  2.730495   0.221318   377.0473 0.5145106 0.47319272
altair.resultPatWNo clearance        2.324182 1.489576 10.218322   1.391342  1302.7825 5.8730349 0.01537452

Likelihood ratio test=9.503886 on 2 df, p=0.008634899, n=235
Wald test = 5.724023 on 2 df, p = 0.05715369

Covariance-Matrix:
                                     altair.resultPatWTransient clearance altair.resultPatWNo clearance
altair.resultPatWTransient clearance                             2.589926                      2.162201
altair.resultPatWNo clearance                                    2.162201                      2.218837
cox_fit_summary <- summary(cox_fit)
coxphf(formula = surv_object ~ altair.resultPatW, data = circ_data)

Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood 

                                         coef se(coef) exp(coef) lower 0.95 upper 0.95     Chisq          p
altair.resultPatWTransient clearance 1.004483 1.609325  2.730495   0.221318   377.0473 0.5145106 0.47319272
altair.resultPatWNo clearance        2.324182 1.489576 10.218322   1.391342  1302.7825 5.8730349 0.01537452

Likelihood ratio test=9.503886 on 2 df, p=0.008634899, n=235
Wald test = 5.724023 on 2 df, p = 0.05715369

Covariance-Matrix:
                                     altair.resultPatWTransient clearance altair.resultPatWNo clearance
altair.resultPatWTransient clearance                             2.589926                      2.162201
altair.resultPatWNo clearance                                    2.162201                      2.218837

#DFS1 by TAS vs Placebo - Central review data - All stages & stratified for Stage & ctDNA 1mo post-surgery

rm(list = ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20250903 Central Imaging Dataset.csv")
circ_data <- as.data.frame(circ_data)

circ_data$altair.Arm    <- factor(circ_data$altair.Arm, levels = c("Control","Experimental"), labels = c("Placebo","FTD/TPI"))
circ_data$Disease.Stage <- factor(circ_data$Disease.Stage, levels = c("Stage II or lower","StageIII","M1"))
circ_data$ctDNA1mo      <- factor(circ_data$ctDNA1mo, levels = c("NEGATIVE","POSITIVE"), labels = c("Negative","Positive"))

event_summary <- circ_data %>% group_by(altair.Arm) %>% summarise(Total = n(), Events = sum(p_evtDFS1b), Fraction = Events / n(), Percentage = (Events / n()) * 100)
print(event_summary)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data, conf.int = 0.95, conf.type = "log-log")

# --- Stratified log-rank test (exactly as in primary analysis) ---
# Use survdiff with the same stratification factors
sd_strat <- survdiff(surv_object ~ altair.Arm + strata(Disease.Stage, ctDNA1mo), data = circ_data)
# For two groups, df = 1
p_strat_logrank <- 1 - pchisq(sd_strat$chisq, df = 1)

# KM plot with the stratified p-value displayed
ggsurvplot(KM_curve, data = circ_data, pval = sprintf("Stratified log-rank p = %.4f", p_strat_logrank), conf.int = FALSE, risk.table = TRUE, break.time.by = 6, palette = c("red","blue"), title = "DFS1 by Arm - All Patients", ylab = "Disease-Free Survival", xlab = "Time from Enrollment (Months)", legend.labs = c("Placebo", "FTD/TPI"), legend.title = "")

print(summary(KM_curve, times = c(6, 12, 18, 24)))
Call: survfit(formula = surv_object ~ altair.Arm, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                altair.Arm=Placebo 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     52      67    0.444  0.0453       0.3542        0.530
   12     28      20    0.269  0.0412       0.1923        0.352
   18     16       6    0.210  0.0386       0.1397        0.290
   24      5       5    0.127  0.0379       0.0649        0.212

                altair.Arm=FTD/TPI 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    6     84      34    0.716  0.0413       0.6254        0.788
   12     34      46    0.318  0.0434       0.2347        0.403
   18     19      10    0.222  0.0395       0.1494        0.303
   24     11       4    0.166  0.0381       0.0996        0.247
cox_fit_stratified <- coxph(surv_object ~ altair.Arm + strata(Disease.Stage) + strata(ctDNA1mo), data = circ_data)
cox_fit_summary_stratified <- summary(cox_fit_stratified)
print(cox_fit_summary_stratified)
Call:
coxph(formula = surv_object ~ altair.Arm + strata(Disease.Stage) + 
    strata(ctDNA1mo), data = circ_data)

  n= 243, number of events= 196 

                     coef exp(coef) se(coef)      z Pr(>|z|)  
altair.ArmFTD/TPI -0.3048    0.7372   0.1462 -2.085   0.0371 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
altair.ArmFTD/TPI    0.7372      1.356    0.5536    0.9819

Concordance= 0.565  (se = 0.021 )
Likelihood ratio test= 4.34  on 1 df,   p=0.04
Wald test            = 4.35  on 1 df,   p=0.04
Score (logrank) test = 4.38  on 1 df,   p=0.04
HR_stratified       <- cox_fit_summary_stratified$conf.int["altair.ArmFTD/TPI","exp(coef)"]
lower_CI_stratified <- cox_fit_summary_stratified$conf.int["altair.ArmFTD/TPI","lower .95"]
upper_CI_stratified <- cox_fit_summary_stratified$conf.int["altair.ArmFTD/TPI","upper .95"]

# --- Use SCORE (log-rank) p-value from the Cox fit ---
if (!is.null(cox_fit_summary_stratified$score)) {
  score_stat <- cox_fit_summary_stratified$score
} else {
  score_stat <- as.numeric(cox_fit_summary_stratified$sctest["test"])
}
p_value_score <- 1 - pchisq(score_stat, df = 1)

# (Optional) Wald p-value for reference
p_value_wald <- as.numeric(cox_fit_summary_stratified$wald["pvalue"])

# Label using SCORE p-value
label_text_stratified <- paste0(
  "HR = ", round(HR_stratified, 2),
  " (", round(lower_CI_stratified, 2), "-",
  round(upper_CI_stratified, 2), "); p (score) = ",
  format.pval(p_value_score, digits = 3)
)
print(label_text_stratified)
[1] "HR = 0.74 (0.55-0.98); p (score) = 0.036"
#print both p-values side-by-side:
cat(sprintf("Stratified log-rank (survdiff) p = %.4f\n", p_strat_logrank))
Stratified log-rank (survdiff) p = 0.0406
cat(sprintf("Cox score-test p = %.4f (Wald p = %.4f)\n", p_value_score, p_value_wald))
Cox score-test p = 0.0364 (Wald p = 0.0371)
---
title: "Bando et al 2025_Altair Final Clinical Analysis"
output: html_notebook
---

library(swimplot)
library(coxphf)
library(grid)
library(gtable)
library(readr) 
library(mosaic)
library(dplyr) 
library(survival) 
library(survminer)
library(gridtext)
library(ggplot2)
library(scales)
library(officer)
library(ggthemes)
library(tidyverse)
library(gtsummary)
library(flextable)
library(parameters)
library(car)
library(grid)
library(ComplexHeatmap)
library(readxl)
library(janitor)
library(rms)
library(pROC)
library(DT)

#Demographics Table by Altair Arm
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")

circ_data_subset1 <- circ_data %>%
  select(
    Age.Group,
    Sex,
    PrimSitev2,
    StageA.alt,
    p_hadNeo,
    p_TxAdjAltair,
    ctDNA1mo,
    p_AltBaselineWin,
    BRAF.V600E,
    RAS,
    MSI) %>%
  mutate(
    Age.Group = factor(Age.Group, levels = c("1", "2"), labels = c("<70", ">70")),
    Sex = factor(Sex, levels = c("Male", "Female")),
    PrimSitev2 = factor(PrimSitev2, levels = c("Right-sided colon", "Left-sided colon", "Rectum")),
    StageA.alt = factor(StageA.alt, levels = c("I", "II", "III", "IV")),
    p_hadNeo = factor(p_hadNeo, levels=c("FALSE","TRUE"), labels = c("No", "Yes")),
    p_TxAdjAltair = factor(p_TxAdjAltair, levels=c("FALSE","TRUE"), labels = c("No", "Yes")),
    ctDNA1mo = factor(ctDNA1mo, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive")),
    p_AltBaselineWin = factor(p_AltBaselineWin, levels = c("MRD", "OnTreatment", "Surveillance")),
    BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF wt", "BRAF V600E")),
    RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS wt", "RAS mut")),
    MSI = factor(MSI, levels = c("MSS", "MSI-High")))

circ_data1 <- read.csv("Altair 20240729 Dataset.csv")

circ_data_subset2 <- circ_data1 %>%
  select(
    Age.Group,
    Sex,
    PrimSitev2,
    StageA.alt,
    p_hadNeo,
    p_TxAdjAltair,
    ctDNA1mo,
    p_AltBaselineWin,
    BRAF.V600E,
    RAS,
    MSI,
    altair.Arm) %>%
  mutate(
    Age.Group = factor(Age.Group, levels = c("1", "2"), labels = c("<70", ">70")),
    Sex = factor(Sex, levels = c("Male", "Female")),
    PrimSitev2 = factor(PrimSitev2, levels = c("Right-sided colon", "Left-sided colon", "Rectum")),
    StageA.alt = factor(StageA.alt, levels = c("I", "II", "III", "IV")),
    p_hadNeo = factor(p_hadNeo, levels=c("FALSE","TRUE"), labels = c("No", "Yes")),
    p_TxAdjAltair = factor(p_TxAdjAltair, levels=c("FALSE","TRUE"), labels = c("No", "Yes")),
    ctDNA1mo = factor(ctDNA1mo, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive")),
    p_AltBaselineWin = factor(p_AltBaselineWin, levels = c("MRD", "OnTreatment", "Surveillance")),
    BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF wt", "BRAF V600E")),
    RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS wt", "RAS mut")),
    MSI = factor(MSI, levels = c("MSS", "MSI-High")),
    altair.Arm = factor(altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI")))
Overall <- circ_data_subset1 %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
Overall

ByctDNA_MRD <- circ_data_subset2 %>%
  tbl_summary(
    by = altair.Arm, # add this line to subgroup by altair.Arm
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels()
ByctDNA_MRD

merged_table <- tbl_merge(tbls=list(Overall, ByctDNA_MRD))
merged_table

fit1 <- as_flex_table(
  merged_table,
  include = everything(),
  return_calls = FALSE
)
fit1
save_as_docx(fit1, path = "~/Downloads/merged_table.docx")
```

#Median enrollment MTM/mL in the complete cohort
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)

median_val <- median(circ_data$p_AltBaselineMTM, na.rm = TRUE)
q1_val <- quantile(circ_data$p_AltBaselineMTM, 0.25, na.rm = TRUE)
q3_val <- quantile(circ_data$p_AltBaselineMTM, 0.75, na.rm = TRUE)
range_val <- range(circ_data$p_AltBaselineMTM, na.rm = TRUE)
cat("Median:", format(median_val, digits = 4), "\n")
cat("Q1 (25th percentile):", q1_val, "\n")
cat("Q3 (75th percentile):", q3_val, "\n")
cat("Range:", range_val, "\n")
```

#Median enrollment MTM/mL by Stage
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)
circ_data$StageA.alt <- factor(circ_data$StageA.alt, levels = c("I", "II", "III", "IV"))

stage_summary <- circ_data %>%
  group_by(StageA.alt) %>%
  summarise(
    Median = median(p_AltBaselineMTM, na.rm = TRUE),
    Q1 = quantile(p_AltBaselineMTM, 0.25, na.rm = TRUE),
    Q3 = quantile(p_AltBaselineMTM, 0.75, na.rm = TRUE),
    Min = min(p_AltBaselineMTM, na.rm = TRUE),
    Max = max(p_AltBaselineMTM, na.rm = TRUE)
  ) %>%
  ungroup()
print(stage_summary)
```

#Median enrollment MTM/mL by enrollment window
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)
circ_data$p_AltBaselineWin <- factor(circ_data$p_AltBaselineWin, levels = c("MRD", "OnTreatment", "Surveillance"))

stage_summary <- circ_data %>%
  group_by(p_AltBaselineWin) %>%
  summarise(
    Median = median(p_AltBaselineMTM, na.rm = TRUE),
    Q1 = quantile(p_AltBaselineMTM, 0.25, na.rm = TRUE),
    Q3 = quantile(p_AltBaselineMTM, 0.75, na.rm = TRUE),
    Min = min(p_AltBaselineMTM, na.rm = TRUE),
    Max = max(p_AltBaselineMTM, na.rm = TRUE)
  ) %>%
  ungroup()
print(stage_summary)
```

#Median enrollment MTM/mL by enrollment window in each treatment arm
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)
circ_data$p_AltBaselineWin <- factor(circ_data$p_AltBaselineWin, levels = c("MRD", "OnTreatment", "Surveillance"))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels = c("Control", "Experimental"), labels = c("Placebo", "FTD/TPI"))

# 1. Summary statistics by p_AltBaselineWin
stage_summary <- circ_data %>%
  group_by(p_AltBaselineWin) %>%
  summarise(
    Median = median(p_AltBaselineMTM, na.rm = TRUE),
    Q1 = quantile(p_AltBaselineMTM, 0.25, na.rm = TRUE),
    Q3 = quantile(p_AltBaselineMTM, 0.75, na.rm = TRUE),
    Min = min(p_AltBaselineMTM, na.rm = TRUE),
    Max = max(p_AltBaselineMTM, na.rm = TRUE)
  ) %>%
  ungroup()
print("Summary by p_AltBaselineWin:")
print(stage_summary)

# 2. Median by p_AltBaselineWin and altair.Arm
arm_stage_summary <- circ_data %>%
  group_by(p_AltBaselineWin, altair.Arm) %>%
  summarise(
    Median = median(p_AltBaselineMTM, na.rm = TRUE),
    n = n()
  ) %>%
  ungroup()
print("Median p_AltBaselineMTM by p_AltBaselineWin and altair.Arm:")
print(arm_stage_summary)

# 3. Wilcoxon test p-values for Control vs Experimental within each p_AltBaselineWin
p_values <- circ_data %>%
  filter(!is.na(p_AltBaselineMTM), !is.na(altair.Arm), !is.na(p_AltBaselineWin)) %>%
  group_by(p_AltBaselineWin) %>%
  summarise(
    p_value = tryCatch(
      wilcox.test(p_AltBaselineMTM ~ altair.Arm)$p.value,
      error = function(e) NA
    )
  )
print("Wilcoxon test p-values by p_AltBaselineWin:")
print(p_values)
```

#Median enrollment MTM/mL in Stage IV vs Non-Stage IV
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[!(circ_data$StageA.alt %in% c("I", "II", "III")),]
circ_datadf <- as.data.frame(circ_data)

median_val <- median(circ_data$p_AltBaselineMTM, na.rm = TRUE)
q1_val <- quantile(circ_data$p_AltBaselineMTM, 0.25, na.rm = TRUE)
q3_val <- quantile(circ_data$p_AltBaselineMTM, 0.75, na.rm = TRUE)
range_val <- range(circ_data$p_AltBaselineMTM, na.rm = TRUE)
cat("Median:", format(median_val, digits = 4), "\n")
cat("Q1 (25th percentile):", q1_val, "\n")
cat("Q3 (75th percentile):", q3_val, "\n")
cat("Range:", range_val, "\n")

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)
circ_data$Stage.Final <- factor(circ_data$Stage.Final, levels = c("I-III", "IV"))
median_MTM <- aggregate(p_AltBaselineMTM ~ Stage.Final, data = circ_data, FUN = median)
print(median_MTM)
circ_data$Stage.Final <- factor(circ_data$Stage.Final, levels = c("I-III", "IV"))
boxplot(p_AltBaselineMTM~Stage.Final, data=circ_data, main="MTM/mL at enrollment", xlab="Stage", ylab="MTM/mL", col="white",border="black")
m1<-wilcox.test(p_AltBaselineMTM ~ Stage.Final, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)
```

#Number of patients with enrolment MTM/mL > various thresholds
```{r}
rm(list = ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data$p_AltBaselineMTM <- as.numeric(circ_data$p_AltBaselineMTM)

# Define your cutoffs
cutoffs <- c(0.01, 0.047, 0.1, 0.179, 0.2, 0.3, 0.5, 1, 5, 8.172, 10)
total_pts <- nrow(circ_data)
for (co in cutoffs) {
  pts_above <- sum(circ_data$p_AltBaselineMTM >= co, na.rm = TRUE)
  perc      <- (pts_above / total_pts) * 100
  cat("Cutoff:", co, 
      "- Patients ≥ cutoff:", pts_above, 
      "- Percentage:", round(perc, 2), "%\n")
}
```

#DFS1 by TAS vs Placebo - All stages & stratified for Stage & ctDNA 1mo post-surgery
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - All Patients", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
circ_data$Disease.Stage <- factor(circ_data$Disease.Stage, levels=c("Stage II or lower","StageIII", "M1"))
circ_data$ctDNA1mo <- factor(circ_data$ctDNA1mo, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
cox_fit_stratified <- coxph(surv_object ~ altair.Arm + strata(Disease.Stage) + strata(ctDNA1mo), data = circ_data)
summary(cox_fit_stratified)

# Extract values for HR, 95% CI, and p-value
cox_fit_summary_stratified <- summary(cox_fit_stratified)
HR_stratified <- cox_fit_summary_stratified$coefficients[2]
lower_CI_stratified <- cox_fit_summary_stratified$conf.int[3]
upper_CI_stratified <- cox_fit_summary_stratified$conf.int[4]
p_value_stratified <- cox_fit_summary_stratified$coefficients[5]
label_text_stratified <- paste0("HR = ", round(HR_stratified, 2), 
                                " (", round(lower_CI_stratified, 2), "-", 
                                round(upper_CI_stratified, 2), "); p = ", 
                                round(p_value_stratified, 3))
print(label_text_stratified)
```




#DFS1 by TAS vs Placebo - Excluding QC patients
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$QC.Exclude=="FALSE",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - Excluding those with QC Revisions", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#DFS1 by TAS vs Placebo - Excluding Mets patients
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$Mets.Exclude=="FALSE",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - Excluding those with Mets prior to enrolment", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#DFS1 by TAS vs Placebo - Stage I-III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[!(circ_data$StageA.alt %in% c("IV")),]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - Stage I-III", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#DFS1 by TAS vs Placebo - Stage I-II
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[!(circ_data$StageA.alt %in% c("III","IV")),]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - Stage I-II", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#DFS1 by TAS vs Placebo - Stage III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[!(circ_data$StageA.alt %in% c("I", "II", "IV")),]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - Stage III", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#DFS1 by TAS vs Placebo - Stage IV
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[!(circ_data$StageA.alt %in% c("I", "II", "III")),]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - Stage IV", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```



#DFS1 by TAS vs Placebo - ctDNA positive post-surgery
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$ctDNA1mo == "POSITIVE",]
circ_data <- subset(circ_data, !is.na(p_MRD))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - ctDNA positive post-surgery", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Fisher test for DFS percentages at 6, 12, 18 and 24 months
dfs_times <- c(6, 12, 18, 24)
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  pos_count <- sum(circ_data$altair.Arm == "Placebo" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  neg_total <- sum(circ_data$altair.Arm == "FTD/TPI")
  pos_total <- sum(circ_data$altair.Arm == "Placebo")
  
  neg_surv <- neg_total - sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  pos_surv <- pos_total - sum(circ_data$altair.Arm == "Placebo" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)
```

#DFS1 by TAS vs Placebo - ctDNA negative post-surgery
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$ctDNA1mo == "NEGATIVE",]
circ_data <- subset(circ_data, !is.na(ctDNA1mo))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - ctDNA negative post-surgery", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#DFS2 by TAS vs Placebo - All stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS2.months, event = circ_data$p_evtDFS2)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS2),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS2.months, event = circ_data$p_evtDFS2)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS2 by Arm", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#OS by TAS vs Placebo - All stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$p_evtOS)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtOS),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$p_evtOS)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="OS by Arm - All Patients", ylab= "Overall Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```
#DFS1 by ctDNA MRD enrollment timepoint TAS vs Placebo
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$p_AltBaselineWin=="MRD",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - ctDNA MRD Enrollment", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Fisher test for DFS percentages at 6, 12, 18 and 24 months
dfs_times <- c(6, 12, 18, 24)
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  pos_count <- sum(circ_data$altair.Arm == "Placebo" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  neg_total <- sum(circ_data$altair.Arm == "FTD/TPI")
  pos_total <- sum(circ_data$altair.Arm == "Placebo")
  
  neg_surv <- neg_total - sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  pos_surv <- pos_total - sum(circ_data$altair.Arm == "Placebo" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)
```


#DFS1 by ctDNA On-treatment timepoint TAS vs Placebo
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$p_AltBaselineWin=="OnTreatment",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - ctDNA On-treatment Enrollment", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Fisher test for DFS percentages at 6, 12, 18 and 24 months
dfs_times <- c(6, 12, 18, 24)
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  pos_count <- sum(circ_data$altair.Arm == "Placebo" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  neg_total <- sum(circ_data$altair.Arm == "FTD/TPI")
  pos_total <- sum(circ_data$altair.Arm == "Placebo")
  
  neg_surv <- neg_total - sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  pos_surv <- pos_total - sum(circ_data$altair.Arm == "Placebo" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)
```


#DFS1 by ctDNA Surveillance timepoint TAS vs Placebo
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$p_AltBaselineWin=="Surveillance",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - ctDNA Surveillance Enrollment", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Fisher test for DFS percentages at 6, 12, 18 and 24 months
dfs_times <- c(6, 12, 18, 24)
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  pos_count <- sum(circ_data$altair.Arm == "Placebo" & circ_data$DFS.months >= time & circ_data$p_evtDFS1b == 0)
  neg_total <- sum(circ_data$altair.Arm == "FTD/TPI")
  pos_total <- sum(circ_data$altair.Arm == "Placebo")
  
  neg_surv <- neg_total - sum(circ_data$altair.Arm == "FTD/TPI" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  pos_surv <- pos_total - sum(circ_data$altair.Arm == "Placebo" & circ_data$p_evtDFS1b == 1 & circ_data$DFS.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)
```


#Barplot with enrollment timepoint at any time by Arm
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")

circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
circ_data$p_AltBaselineWin <- factor(circ_data$p_AltBaselineWin, levels = c("MRD", "OnTreatment", "Surveillance"), labels = c("MRD", "On Treatment", "Surveillance"))
contingency_table <- table(circ_data$altair.Arm, circ_data$p_AltBaselineWin)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
p_values <- c(chi_square_test$p.value, fisher_exact_test$p.value)
p_adjusted <- p.adjust(p_values, method = "bonferroni")
names(p_adjusted) <- c("Chi-Square Test", "Fisher's Exact Test")
print(p_adjusted)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "Enrollment timepoint", 
       x = "Arm", 
       y = "Patients (%)", 
       fill = "Enrollment timepoint",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Surveillance" = "lightblue", "On Treatment" = "lightgreen", "MRD" = "salmon")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate median MTM/mL for enrollment timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
result <- circ_data %>%
  group_by(p_AltBaselineWin) %>%
  summarise(
    Median = median(p_AltBaselineMTM, na.rm = TRUE),
    Range = paste(min(p_AltBaselineMTM, na.rm = TRUE), max(p_AltBaselineMTM, na.rm = TRUE), sep = " - ")
  )
print(result)
```

#DFS1 by TAS vs Placebo - All stages MTM/mL based on the lowest MTM/mL upon which the trial is positive
```{r}
#Pts with MTM/mL≥0.047
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")

total_pts <- nrow(circ_data)
pts_MTM <- nrow(circ_data[circ_data$p_AltBaselineMTM >= 0.047,])
percentage_pts_MTM <- (pts_MTM / total_pts) * 100
print(paste0("Percentage of patients with MTM ≥ 0.047: ", round(percentage_pts_MTM, 2), "%"))

circ_data <- circ_data[circ_data$p_AltBaselineMTM>=0.047,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - MTM/mL ≥0.047", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Pts with MTM/mL<0.047
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")

total_pts <- nrow(circ_data)
pts_MTM <- nrow(circ_data[circ_data$p_AltBaselineMTM < 0.047,])
percentage_pts_MTM <- (pts_MTM / total_pts) * 100
print(paste0("Percentage of patients with MTM < 0.047: ", round(percentage_pts_MTM, 2), "%"))

circ_data <- circ_data[circ_data$p_AltBaselineMTM<0.047,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.Arm, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.Arm) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS1 by Arm - MTM/mL <0.047", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("Placebo", "FTD/TPI"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
cox_fit <- coxph(surv_object ~ altair.Arm, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Analysis for Likelihood-Ratio Interaction P value
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data$p_evtDFS1b  <- as.logical(circ_data$p_evtDFS1b)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)

circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))
circ_data$ctDNA.MTM <- NA
circ_data <- circ_data %>%
  mutate(ctDNA.MTM = case_when(
    p_AltBaselineMTM<0.047 ~ 1,
    p_AltBaselineMTM>=0.047 ~ 2
  ))
circ_data$ctDNA.MTM <- factor(circ_data$ctDNA.MTM, levels=c("1","2"), labels = c("<0.047", "≥0.047"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
cox_model_main <- coxph(surv_object ~ ctDNA.MTM + altair.Arm, data = circ_data)
cox_model_interaction <- coxph(surv_object ~ ctDNA.MTM * altair.Arm, data = circ_data)
lrt_result <- anova(cox_model_main, cox_model_interaction, test = "LRT")
print(lrt_result)
```




#DFS1 by TAS vs Placebo - MTM/mL as continuous variable
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data$p_evtDFS1b  <- as.logical(circ_data$p_evtDFS1b)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$p_AltBaselineMTM <- as.numeric(circ_data$p_AltBaselineMTM)
circ_data$altair.Arm <- factor(circ_data$altair.Arm, levels=c("Control","Experimental"), labels = c("Placebo", "FTD/TPI"))

#############################################################################
# 3. Define cutoffs (0.01 to 100 on a normal numeric scale)
#############################################################################
cutoffs <- seq(0.01, 100, length.out = 50)

# Prepare a data frame to store results
results_df <- data.frame(
  cutoff = cutoffs,
  HR     = NA_real_,
  HR_low = NA_real_,
  HR_hi  = NA_real_,
  pval   = NA_real_,
  n_included = NA_integer_
)

#############################################################################
# 4. Loop over each cutoff: subselect data and fit Cox model (Placebo vs. TAS-102)
#############################################################################
for (i in seq_along(cutoffs)) {
  
  current_cutoff <- cutoffs[i]
  
  # Subset: patients with p_AltBaselineMTM >= current_cutoff
  sub_data <- circ_data %>%
    filter(p_AltBaselineMTM >= current_cutoff)
  
  # Re-factor in case any level is dropped
  sub_data$altair.Arm <- factor(sub_data$altair.Arm, levels = c("Placebo", "FTD/TPI"))
  
  # Only run the Cox model if both arms have at least some minimal data
  arm_counts <- table(sub_data$altair.Arm)
  if (length(arm_counts) == 2 && all(arm_counts >= 2)) {
    
    fit <- coxph(Surv(DFS.months, p_evtDFS1b) ~ altair.Arm, data = sub_data)
    fit_sum <- summary(fit)
    
    # Extract HR, 95% CI, and p-value
    hr          <- fit_sum$conf.int[,"exp(coef)"][1]
    hr_conf_low <- fit_sum$conf.int[,"lower .95"][1]
    hr_conf_hi  <- fit_sum$conf.int[,"upper .95"][1]
    pval        <- fit_sum$coefficients[,"Pr(>|z|)"][1]
    
    # Store in results_df
    results_df$HR[i]     <- hr
    results_df$HR_low[i] <- hr_conf_low
    results_df$HR_hi[i]  <- hr_conf_hi
    results_df$pval[i]   <- pval
    results_df$n_included[i] <- nrow(sub_data)
    
  } else {
    results_df$n_included[i] <- nrow(sub_data)
  }
}

#############################################################################
# 5. Plot: x-axis on log scale, y-axis with breaks at 0.05, 0.1, 0.25, 0.5, 1, 2
#############################################################################
plot_df <- results_df %>%
  filter(!is.na(HR))

# Plot
p <- ggplot(plot_df, aes(x = cutoff, y = HR)) +
  # Ribbon for confidence intervals
  geom_ribbon(aes(ymin = HR_low, ymax = HR_hi), alpha = 0.2) +
  # Line for the HR
  geom_line(size = 1) +
  # Reference line at HR=1
  geom_hline(yintercept = 1, linetype = "dashed", color = "red") +
  
  # X-axis on log scale
  scale_x_log10(
    breaks = c(0.01, 0.1, 1, 10, 100), 
    labels = c("0.01", "0.1", "1", "10", "100")
  ) +
  
  # Y-axis on normal (linear) scale with specific breaks
  scale_y_log10(
    breaks = c(0.1, 0.3, 1, 3),
    labels = c("0.1", "0.3", "1", "3"),
    limits = c(0.1, 3)   # Adjust or remove if needed
  ) +
  
  theme_bw(base_size = 14) +
  labs(
    title = "Hazard Ratio (Placebo vs. TAS-102) by MTM/mL",
    x     = "MTM/mL (log scale)",
    y     = "Hazard Ratio (log scale)"
  )

print(p)

# Find the 2nd MTM cutoff where the upper bound of the confidence interval (HR_hi) crosses HR = 1
crossing_point <- results_df %>% 
  filter(HR_hi >= 1) %>% 
  slice(2)  # Select the 2nd occurrence

# Print the cutoff value where HR_hi crosses 1
print(crossing_point$cutoff)

#############################################################################
# 6. (Optional) Add vertical lines for specific cutoffs
#############################################################################
p + 
  geom_vline(
    xintercept = c(0.047, 0.179, 8.172),
    linetype   = "dashed",
    color      = "blue"
  ) +
  annotate(
    "text", 
    x     = c(0.179, 8.172), 
    y     = max(plot_df$HR_hi, na.rm = TRUE),
    label = c("0.179", "8.172"), 
    vjust = -0.5, 
    color = "blue"
  )
```


#Histogram for number of patients per enrolment MTM/mL
```{r}
rm(list = ls())
setwd("~/Downloads")

df <- read.csv("Altair 20240729 Dataset.csv")
df$p_evtDFS1b      <- as.logical(df$p_evtDFS1b)
df$DFS.months      <- as.numeric(df$DFS.months)
df$p_AltBaselineMTM <- as.numeric(df$p_AltBaselineMTM)

# Clean the data
df <- df %>% filter(!is.na(p_AltBaselineMTM), p_AltBaselineMTM > 0)

# Descriptive stats (note: use the correct column name)
ppm_range  <- range(df$p_AltBaselineMTM)
lowest_ppm <- ppm_range[1]
highest_ppm <- ppm_range[2]
median_ppm <- median(df$p_AltBaselineMTM)

cat("Lowest PPM value :", lowest_ppm, "\n")
cat("Highest PPM value:", highest_ppm, "\n")
cat("Median PPM value :", median_ppm, "\n")
cat("Full range       :", lowest_ppm, "to", highest_ppm, "\n\n")

# Histogram with custom log‑scale breaks
ggplot(df, aes(x = p_AltBaselineMTM)) +
  geom_histogram(bins = 100, fill = "gray80", color = "black") +
  scale_x_log10(
    breaks  = c(0.01, 0.1, 1, 10, 100),
    labels  = c("0.01", "0.1", "1", "10", "100")
  ) +
  labs(x = "p_AltBaselineMTM", y = "Number of samples") +
  ylim(0, 10) +
  theme_minimal()
```

#Enrollment MTM/mL by ctDNA clearance in TAS-102 vs Placebo Arms
```{r}
#Placebo
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$altair.group.SAP_MSv2!="1b = Exclude: No on-Tx TPs",]
circ_data <- circ_data[circ_data$altair.Arm=="Control",]

# Transform p_MRD_MTM with log10
circ_data$p_AltBaselineMTM <- as.numeric(as.character(circ_data$p_AltBaselineMTM))
circ_data$p_evtDFS1b <- factor(circ_data$p_evtDFS1b, levels=c("TRUE","FALSE"), labels = c("Recurrence", "No Recurrence"))
circ_data$altair.resultPatW <- factor(circ_data$altair.resultPatW, levels=c("No clearance", "Transient clearance", "Sustained clearance"))
median_p_MRD_MTM <- aggregate(p_AltBaselineMTM ~ altair.resultPatW, data = circ_data, FUN = median)
print(median_p_MRD_MTM)

# Create violin plot with log10 scale on y-axis
ggplot(circ_data, aes(x=altair.resultPatW, y=p_AltBaselineMTM, fill=altair.resultPatW)) +
  geom_violin(trim=FALSE) +
  scale_fill_manual(values=c("Sustained clearance"="lightblue", "Transient clearance"="lightgreen", "No clearance"="salmon")) +
  geom_boxplot(width=0.1, fill="white", colour="black", alpha=0.5) +
  scale_y_log10(breaks=c(0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000)) +
  labs(title="Enrollment MTM/mL | Clearance - Placebo Arm", x="Clearance", y="Enrollment MTM/mL") +
  theme_minimal() +
  theme(legend.position="none")
m3_1v2 <- wilcox.test(p_AltBaselineMTM ~ altair.resultPatW,
                      data = circ_data[circ_data$altair.resultPatW %in% c("Sustained clearance", "Transient clearance"), ],
                      na.rm = TRUE)
print(m3_1v2)
m3_1v3 <- wilcox.test(p_AltBaselineMTM ~ altair.resultPatW,
                      data = circ_data[circ_data$altair.resultPatW %in% c("Sustained clearance", "No clearance"), ],
                      na.rm = TRUE)
print(m3_1v3)
m3_2v3 <- wilcox.test(p_AltBaselineMTM ~ altair.resultPatW,
                      data = circ_data[circ_data$altair.resultPatW %in% c("Transient clearance", "No clearance"), ],
                      na.rm = TRUE)
print(m3_2v3)

# Create a table with p-values
p_value_table <- data.frame(
  Comparison = c("Sustained vs Transient", "Sustained vs No Clearance", "Transient vs No Clearance"),
  P_Value = c(m3_1v2$p.value, m3_1v3$p.value, m3_2v3$p.value)
)
print(p_value_table)

#TAS-102
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$altair.group.SAP_MSv2!="1b = Exclude: No on-Tx TPs",]
circ_data <- circ_data[circ_data$altair.Arm=="Experimental",]

# Transform p_MRD_MTM with log10
circ_data$p_AltBaselineMTM <- as.numeric(as.character(circ_data$p_AltBaselineMTM))
circ_data$p_evtDFS1b <- factor(circ_data$p_evtDFS1b, levels=c("TRUE","FALSE"), labels = c("Recurrence", "No Recurrence"))
circ_data$altair.resultPatW <- factor(circ_data$altair.resultPatW, levels=c("No clearance", "Transient clearance", "Sustained clearance"))
median_p_MRD_MTM <- aggregate(p_AltBaselineMTM ~ altair.resultPatW, data = circ_data, FUN = median)
print(median_p_MRD_MTM)

# Create violin plot with log10 scale on y-axis
ggplot(circ_data, aes(x=altair.resultPatW, y=p_AltBaselineMTM, fill=altair.resultPatW)) +
  geom_violin(trim=FALSE) +
  scale_fill_manual(values=c("Sustained clearance"="lightblue", "Transient clearance"="lightgreen", "No clearance"="salmon")) +
  geom_boxplot(width=0.1, fill="white", colour="black", alpha=0.5) +
  scale_y_log10(breaks=c(0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000)) +
  labs(title="Enrollment MTM/mL | Clearance - TAS-102 Arm", x="Clearance", y="Enrollment MTM/mL") +
  theme_minimal() +
  theme(legend.position="none")
m3_1v2 <- wilcox.test(p_AltBaselineMTM ~ altair.resultPatW,
                      data = circ_data[circ_data$altair.resultPatW %in% c("Sustained clearance", "Transient clearance"), ],
                      na.rm = TRUE)
print(m3_1v2)
m3_1v3 <- wilcox.test(p_AltBaselineMTM ~ altair.resultPatW,
                      data = circ_data[circ_data$altair.resultPatW %in% c("Sustained clearance", "No clearance"), ],
                      na.rm = TRUE)
print(m3_1v3)
m3_2v3 <- wilcox.test(p_AltBaselineMTM ~ altair.resultPatW,
                      data = circ_data[circ_data$altair.resultPatW %in% c("Transient clearance", "No clearance"), ],
                      na.rm = TRUE)
print(m3_2v3)

# Create a table with p-values
p_value_table <- data.frame(
  Comparison = c("Sustained vs Transient", "Sustained vs No Clearance", "Transient vs No Clearance"),
  P_Value = c(m3_1v2$p.value, m3_1v3$p.value, m3_2v3$p.value)
)
print(p_value_table)
```

#DFS1 by ctDNA Clearance with 3 groups - All stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$altair.group.SAP_MSv2!="1b = Exclude: No on-Tx TPs",]

circ_data$altair.resultPatW <- factor(circ_data$altair.resultPatW, levels=c("No clearance", "Transient clearance", "Sustained clearance"))
survfit(Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)~altair.resultPatW, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.resultPatW) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtDFS1b),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.resultPatW, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","green","blue"), title="DFS1 by Arm - ctDNA Clearance", ylab= "Disease-Free Survival", xlab="Time from Enrollment (Months)", legend.labs=c("No clearance", "Transient clearance",  "Sustained clearance"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.resultPatW <- factor(circ_data$altair.resultPatW, levels=c("Sustained clearance", "Transient clearance", "No clearance"))
cox_fit <- coxph(surv_object ~ altair.resultPatW, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
```




#OS by ctDNA Clearance with 3 groups - All stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20240729 Dataset.csv")
circ_data <- circ_data[circ_data$altair.group.SAP_MSv2!="1b = Exclude: No on-Tx TPs",]

circ_data$altair.resultPatW <- factor(circ_data$altair.resultPatW, levels=c("No clearance", "Transient clearance", "Sustained clearance"))
survfit(Surv(time = circ_data$OS.months, event = circ_data$p_evtOS)~altair.resultPatW, data = circ_data)
event_summary <- circ_data %>%
  group_by(altair.resultPatW) %>%
  summarise(
    Total = n(),
    Events = sum(p_evtOS),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$p_evtOS)
KM_curve <- survfit(surv_object ~ altair.resultPatW, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","green","blue"), title="OS by Arm - ctDNA Clearance", ylab= "Overall Survival", xlab="Time from Enrollment (Months)", legend.labs=c("No clearance", "Transient clearance",  "Sustained clearance"), legend.title="")
summary(KM_curve, times= c(6, 12, 18, 24))
circ_data$altair.resultPatW <- factor(circ_data$altair.resultPatW, levels=c("Sustained clearance", "Transient clearance", "No clearance"))
cox_fit <- coxphf(surv_object ~ altair.resultPatW, data=circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
```


#DFS1 by TAS vs Placebo - Central review data - All stages & stratified for Stage & ctDNA 1mo post-surgery
```{r}
rm(list = ls())
setwd("~/Downloads")
circ_data <- read.csv("Altair 20250903 Central Imaging Dataset.csv")
circ_data <- as.data.frame(circ_data)

circ_data$altair.Arm    <- factor(circ_data$altair.Arm, levels = c("Control","Experimental"), labels = c("Placebo","FTD/TPI"))
circ_data$Disease.Stage <- factor(circ_data$Disease.Stage, levels = c("Stage II or lower","StageIII","M1"))
circ_data$ctDNA1mo      <- factor(circ_data$ctDNA1mo, levels = c("NEGATIVE","POSITIVE"), labels = c("Negative","Positive"))

event_summary <- circ_data %>% group_by(altair.Arm) %>% summarise(Total = n(), Events = sum(p_evtDFS1b), Fraction = Events / n(), Percentage = (Events / n()) * 100)
print(event_summary)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$p_evtDFS1b)
KM_curve <- survfit(surv_object ~ altair.Arm, data = circ_data, conf.int = 0.95, conf.type = "log-log")

# --- Stratified log-rank test (exactly as in primary analysis) ---
# Use survdiff with the same stratification factors
sd_strat <- survdiff(surv_object ~ altair.Arm + strata(Disease.Stage, ctDNA1mo), data = circ_data)
# For two groups, df = 1
p_strat_logrank <- 1 - pchisq(sd_strat$chisq, df = 1)

# KM plot with the stratified p-value displayed
ggsurvplot(KM_curve, data = circ_data, pval = sprintf("Stratified log-rank p = %.4f", p_strat_logrank), conf.int = FALSE, risk.table = TRUE, break.time.by = 6, palette = c("red","blue"), title = "DFS1 by Arm - All Patients", ylab = "Disease-Free Survival", xlab = "Time from Enrollment (Months)", legend.labs = c("Placebo", "FTD/TPI"), legend.title = "")
print(summary(KM_curve, times = c(6, 12, 18, 24)))
cox_fit_stratified <- coxph(surv_object ~ altair.Arm + strata(Disease.Stage) + strata(ctDNA1mo), data = circ_data)
cox_fit_summary_stratified <- summary(cox_fit_stratified)
print(cox_fit_summary_stratified)
HR_stratified       <- cox_fit_summary_stratified$conf.int["altair.ArmFTD/TPI","exp(coef)"]
lower_CI_stratified <- cox_fit_summary_stratified$conf.int["altair.ArmFTD/TPI","lower .95"]
upper_CI_stratified <- cox_fit_summary_stratified$conf.int["altair.ArmFTD/TPI","upper .95"]

# --- Use SCORE (log-rank) p-value from the Cox fit ---
if (!is.null(cox_fit_summary_stratified$score)) {
  score_stat <- cox_fit_summary_stratified$score
} else {
  score_stat <- as.numeric(cox_fit_summary_stratified$sctest["test"])
}
p_value_score <- 1 - pchisq(score_stat, df = 1)

# (Optional) Wald p-value for reference
p_value_wald <- as.numeric(cox_fit_summary_stratified$wald["pvalue"])

# Label using SCORE p-value
label_text_stratified <- paste0(
  "HR = ", round(HR_stratified, 2),
  " (", round(lower_CI_stratified, 2), "-",
  round(upper_CI_stratified, 2), "); p (score) = ",
  format.pval(p_value_score, digits = 3)
)
print(label_text_stratified)

#print both p-values side-by-side:
cat(sprintf("Stratified log-rank (survdiff) p = %.4f\n", p_strat_logrank))
cat(sprintf("Cox score-test p = %.4f (Wald p = %.4f)\n", p_value_score, p_value_wald))
```



